1
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Fatima N, Rizvi SAM, Rizvi MSBA. Dermatological disease prediction and diagnosis system using deep learning. Ir J Med Sci 2024; 193:1295-1303. [PMID: 38036757 DOI: 10.1007/s11845-023-03578-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023]
Abstract
The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and incidence of skin diseases. Medical technology based on lasers and photonics has made it possible to identify skin illnesses considerably more rapidly and correctly. However, the cost of such a diagnosis is currently limited and prohibitively high and restricted to developed areas. The present paper develops a holistic, critical, and important skin disease prediction system that utilizes machine learning and deep learning algorithms to accurately identify up to 20 different skin diseases with a high F1 score and efficiency. Deep learning algorithms like Xception, Inception-v3, Resnet50, DenseNet121, and Inception-ResNet-v2 were employed to accurately classify diseases based on the images. The training and testing have been performed on an enlarged dataset, and classification was performed for 20 diseases. The algorithm developed was free from any inherent bias and treated all classes equally. The present model, which was trained using the Xception algorithm, is highly efficient and accurate for 20 different skin conditions, with a dataset of over 10,000 photos. The developed system was able to classify 20 different dermatological diseases with high accuracy and precision.
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Affiliation(s)
- Neda Fatima
- Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India.
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2
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Kumar A, Kumar M, Bhardwaj VP, Kumar S, Selvarajan S. A novel skin cancer detection model using modified finch deep CNN classifier model. Sci Rep 2024; 14:11235. [PMID: 38755202 PMCID: PMC11099129 DOI: 10.1038/s41598-024-60954-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
Skin cancer is one of the most life-threatening diseases caused by the abnormal growth of the skin cells, when exposed to ultraviolet radiation. Early detection seems to be more crucial for reducing aberrant cell proliferation because the mortality rate is rapidly rising. Although multiple researches are available based on the skin cancer detection, there still exists challenges in improving the accuracy, reducing the computational time and so on. In this research, a novel skin cancer detection is performed using a modified falcon finch deep Convolutional neural network classifier (Modified Falcon finch deep CNN) that efficiently detects the disease with higher efficiency. The usage of modified falcon finch deep CNN classifier effectively analyzed the information relevant to the skin cancer and the errors are also minimized. The inclusion of the falcon finch optimization in the deep CNN classifier is necessary for efficient parameter tuning. This tuning enhanced the robustness and boosted the convergence of the classifier that detects the skin cancer in less stipulated time. The modified falcon finch deep CNN classifier achieved accuracy, sensitivity, and specificity values of 93.59%, 92.14%, and 95.22% regarding k-fold and 96.52%, 96.69%, and 96.54% regarding training percentage, proving more effective than literary works.
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Affiliation(s)
- Ashwani Kumar
- Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India
| | - Mohit Kumar
- Department of Information Technology, School of Engineering, MIT-ADT University, Pune, 412201, India
| | | | - Sunil Kumar
- Department of CSE, Galgotias College of Engineering & Technology, 1, Knowledge Park-II, Greater Noida, 201310, India
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3
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Xin C, Liu Z, Ma Y, Wang D, Zhang J, Li L, Zhou Q, Xu S, Zhang Y. Transformer guided self-adaptive network for multi-scale skin lesion image segmentation. Comput Biol Med 2024; 169:107846. [PMID: 38184865 DOI: 10.1016/j.compbiomed.2023.107846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/03/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND In recent years, skin lesion has become a major public health concern, and the diagnosis and management of skin lesions depend heavily on the correct segmentation of the lesions. Traditional convolutional neural networks (CNNs) have demonstrated promising results in skin lesion segmentation, but they are limited in their ability to capture distant connections and intricate features. In addition, current medical image segmentation algorithms rarely consider the distribution of different categories in different regions of the image and do not consider the spatial relationship between pixels. OBJECTIVES This study proposes a self-adaptive position-aware skin lesion segmentation model SapFormer to capture global context and fine-grained detail, better capture spatial relationships, and adapt to different positional characteristics. The SapFormer is a multi-scale dynamic position-aware structure designed to provide a more flexible representation of the relationships between skin lesion characteristics and lesion distribution. Additionally, it increases skin lesion segmentation accuracy and decreases incorrect segmentation of non-lesion areas. INNOVATIONS SapFormer designs multiple hybrid transformers for multi-scale feature encoding of skin images and multi-scale positional feature sensing of the encoded features using a transformer decoder to obtain fine-grained features of the lesion area and optimize the regional feature distribution. The self-adaptive feature framework, built upon the transformer decoder module, dynamically and automatically generates parameterizations with learnable properties at different positions. These parameterizations are derived from the multi-scale encoding characteristics of the input image. Simultaneously, this paper utilizes the cross-attention network to optimize the features of the current region according to the features of other regions, aiming to increase skin lesion segmentation accuracy. MAIN RESULTS The ISIC-2016, ISIC-2017, and ISIC-2018 datasets for skin lesions are used as the basis for the experiment. On these datasets, the proposed model has accuracy values of 97.9 %, 94.3 %, and 95.7 %, respectively. The proposed model's IOU values are, in order, 93.2 %, 86.4 %, and 89.4 %. The proposed model's DSC values are 96.4 %, 92.6 %, and 94.3 %, respectively. All three metrics surpass the performance of the majority of state-of-the-art (SOTA) models. SapFormer's metrics on these datasets demonstrate that it can precisely segment skin lesions. Notably, our approach exhibits remarkable noise resistance in non-lesion areas, while simultaneously conducting finer-grained regional feature extraction on the skin lesion image. CONCLUSIONS In conclusion, the integration of a transformer-guided position-aware network into semantic skin lesion segmentation results in a notable performance boost. The ability of our proposed network to capture spatial relationships and fine-grained details proves beneficial for effective skin lesion segmentation. By enhancing lesion localization, feature extraction, quantitative analysis, and classification accuracy, the proposed segmentation model improves the diagnostic efficiency of skin lesion analysis on dermoscopic images. It assists dermatologists in making more accurate and efficient diagnoses, ultimately leading to better patient care and outcomes. This research paves the way for advances in diagnosing and treating skin lesions, promoting better understanding and decision-making in the clinical setting.
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Affiliation(s)
- Chao Xin
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Zhifang Liu
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Yizhao Ma
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Dianchen Wang
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Jing Zhang
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Lingzhi Li
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Qiongyan Zhou
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
| | - Suling Xu
- The First Affiliated Hospital of Ningbo University, Ningbo, 315211, China.
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4
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Kavitha P, Ayyappan G, Jayagopal P, Mathivanan SK, Mallik S, Al-Rasheed A, Alqahtani MS, Soufiene BO. Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach. BMC Bioinformatics 2023; 24:458. [PMID: 38053030 DOI: 10.1186/s12859-023-05584-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/27/2023] [Indexed: 12/07/2023] Open
Abstract
Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.
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Affiliation(s)
- P Kavitha
- Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India
| | - G Ayyappan
- Department of Information Technology, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India
| | - Prabhu Jayagopal
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Sandeep Kumar Mathivanan
- School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA
- Department of Pharmacology and Toxicology, The University of Arizona, Tucson, AZ, 85721, USA
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, University of Leicester, Michael Atiyah Building, Leicester, LE1 7RH, UK
| | - Ben Othman Soufiene
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
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5
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Juan CK, Su YH, Wu CY, Yang CS, Hsu CH, Hung CL, Chen YJ. Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation. Sci Rep 2023; 13:17087. [PMID: 37816815 PMCID: PMC10564722 DOI: 10.1038/s41598-023-42693-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/13/2023] [Indexed: 10/12/2023] Open
Abstract
We aimed to develop an accurate and efficient skin cancer classification system using deep-learning technology with a relatively small dataset of clinical images. We proposed a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet's deep convolutional neural networks were trained using a dataset of 1215 clinical images of skin tumors diagnosed at Taichung and Taipei Veterans General Hospital between 2015 and 2020. The dataset comprised five categories: benign nevus, seborrheic keratosis, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. The SkinFLNet's performance was evaluated using 463 clinical images between January and December 2021. SkinFLNet achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%, outperforming other deep convolutional neural network models. We also compared SkinFLNet's performance with that of three board-certified dermatologists, and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. Our study presents an efficient skin cancer classification system utilizing model fusion and lifelong learning technologies that can be trained on a relatively small dataset. This system can potentially improve skin cancer screening accuracy in clinical practice.
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Affiliation(s)
- Chao-Kuei Juan
- Department of Dermatology, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Dermatology, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Hao Su
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chen-Yi Wu
- Department of Dermatology, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Dermatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chi-Shun Yang
- Department of Pathology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chung-Hao Hsu
- Department of Dermatology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Che-Lun Hung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yi-Ju Chen
- Department of Dermatology, Taichung Veterans General Hospital, Taichung, Taiwan.
- Department of Dermatology, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Post-Baccalaureate Medicine, Chung-Hsing University, Taichung, Taiwan.
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6
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Zhou P, Liu X, Xiong J. Skin lesion image segmentation based on lightweight multi-scale U-shaped network. Biomed Phys Eng Express 2023; 9:055021. [PMID: 37413980 DOI: 10.1088/2057-1976/ace4d0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023]
Abstract
UNet, and more recently medical image segmentation methods, utilize many parameters and computational quantities to achieve higher performance. However, due to the increasing demand for real-time medical image segmentation tasks, it is important to trade between accuracy rates and computational complexity. To this end, we propose a lightweight multi-scale U-shaped network (LMUNet), a multi-scale inverted residual and an asymmetric atrous spatial pyramid pooling-based network for skin lesion image segmentation. We test LMUNet on multiple medical image segmentation datasets, which show that it reduces the number of parameters by 67X and decreases the computational complexity by 48X while obtaining better performance over the partial lightweight networks.
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Affiliation(s)
- Pengfei Zhou
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, People's Republic of China
| | - Xuefeng Liu
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, People's Republic of China
| | - Jichuan Xiong
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, People's Republic of China
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7
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Ahmad N, Shah JH, Khan MA, Baili J, Ansari GJ, Tariq U, Kim YJ, Cha JH. A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI. Front Oncol 2023; 13:1151257. [PMID: 37346069 PMCID: PMC10281646 DOI: 10.3389/fonc.2023.1151257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/19/2023] [Indexed: 06/23/2023] Open
Abstract
Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets-ISIC2018 and HAM10000-have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time.
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Affiliation(s)
- Naveed Ahmad
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Jamal Hussain Shah
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila, Pakistan
- Department of Informatics, University of Leicester, Leicester, United Kingdom
| | - Jamel Baili
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | | | - Usman Tariq
- Department of Management Information Systems, CoBA, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Ye Jin Kim
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Jae-Hyuk Cha
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
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8
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Le PT, Pham BT, Chang CC, Hsu YC, Tai TC, Li YH, Wang JC. Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13081460. [PMID: 37189563 DOI: 10.3390/diagnostics13081460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times. In this study, we introduce a new lightweight segmentation model, the mobile anti-aliasing attention u-net model (MAAU), which features both encoder and decoder paths. The encoder incorporates an anti-aliasing layer and convolutional blocks to reduce the spatial resolution of input images while avoiding shift equivariance. The decoder uses an attention block and decoder module to capture prominent features in each channel. To address data-related problems, we implemented data augmentation methods such as flip, rotation, shear, translate, and color distortions, which enhanced segmentation efficiency in the international Skin Image Collaboration (ISIC) 2018 and PH2 datasets. Our experimental results demonstrated that our approach had fewer parameters, only 4.2 million, while it outperformed various state-of-the-art segmentation methods.
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Affiliation(s)
- Phuong Thi Le
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan
| | - Bach-Tung Pham
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
| | - Ching-Chun Chang
- Department of Computer Science, University of Warwick, Coventry CV47AL, UK
| | - Yi-Chiung Hsu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan
| | - Tzu-Chiang Tai
- Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan
| | - Yung-Hui Li
- AI Research Center, Hon Hai Research Institute, New Taipei City 236, Taiwan
| | - Jia-Ching Wang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
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9
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Dandu R, Vinayaka Murthy M, Ravi Kumar Y. Transfer learning for segmentation with hybrid classification to Detect Melanoma Skin Cancer. Heliyon 2023; 9:e15416. [PMID: 37151638 PMCID: PMC10161578 DOI: 10.1016/j.heliyon.2023.e15416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 04/06/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023] Open
Abstract
Melanoma is an abnormal proliferation of skin cells that arises and develops in most of the cases on surface of skin that is exposed to copious amounts of sunlight. This common type of cancer may develop in areas of the skin that are not exposed to a much abundant sunlight. The research addresses the problem of Segmentation and Classification of Melanoma Skin Cancer. Melanoma is the fifth most common skin cancer lesion. Bio-medical Imaging and Analysis has become more promising, interesting, and beneficial in recent years to address the eventual problems of Melanoma Skin Cancerous Tissues that may develop on Skin Surfaces. The evolved research finds that Attributes Selected for Classification with Color Layout Filter model. The research has produced an optimal result in terms of certain performance metrics accuracy, precision, recall, PRC (what is PRC? Expansion is needed in Abstract), The proposed method has yielded 90.96% of accuracy and 91% percent of precise and 0.91 of recall out of 1.0, 0.95 of ROC AUC, 0.87 of Kappa Statistic, 0.91 of F-Measure. It has been noticed a lowest error with reference to proposed method on certain dataset. Finally, this research recommends that the Attribute Selected Classifier by implementing one of the image enhancement techniques like Color Layout Filter is showing an efficient outcome.
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10
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Ali Z, Naz S, Zaffar H, Choi J, Kim Y. An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:3548. [PMID: 37050607 PMCID: PMC10098854 DOI: 10.3390/s23073548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/03/2023] [Accepted: 03/25/2023] [Indexed: 06/19/2023]
Abstract
Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively.
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Affiliation(s)
- Zeeshan Ali
- R & D Setups, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan
| | - Sheneela Naz
- Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
| | - Hira Zaffar
- Department of Computer Science, Air University, Aerospace and Aviation Kamra Campus, Islamabad 44000, Pakistan
| | - Jaeun Choi
- College of Business, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Yongsung Kim
- Department of Technology Education, Chungnam National University, Daejeon 34134, Republic of Korea
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11
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Gangurde R, Jagota V, Khan MS, Sakthi VS, Boppana UM, Osei B, Kishore KH. Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition. BIOMED RESEARCH INTERNATIONAL 2023; 2023:6970256. [PMID: 36760472 PMCID: PMC9904903 DOI: 10.1155/2023/6970256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/08/2022] [Accepted: 11/24/2022] [Indexed: 02/03/2023]
Abstract
The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction.
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Affiliation(s)
- Roshan Gangurde
- School of Computer Science, MIT World Peace University, Pune, India
| | - Vishal Jagota
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | | | - Viji Siva Sakthi
- Zoology Department and Research Centre, Sarah Tucker College (Autonomous), Affiliated to Manonmaniam Sundaranar University, Tirunelveli, India
| | | | - Bernard Osei
- Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Kakarla Hari Kishore
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
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12
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Alwakid G, Gouda W, Humayun M, Sama NU. Melanoma Detection Using Deep Learning-Based Classifications. Healthcare (Basel) 2022; 10:healthcare10122481. [PMID: 36554004 PMCID: PMC9777935 DOI: 10.3390/healthcare10122481] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image's quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study's results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.
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Affiliation(s)
- Ghadah Alwakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
- Correspondence:
| | - Walaa Gouda
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
| | - Najm Us Sama
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia
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Aldhyani THH, Verma A, Al-Adhaileh MH, Koundal D. Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12092048. [PMID: 36140447 PMCID: PMC9497471 DOI: 10.3390/diagnostics12092048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Skin is the primary protective layer of the internal organs of the body. Nowadays, due to increasing pollution and multiple other factors, various types of skin diseases are growing globally. With variable shapes and multiple types, the classification of skin lesions is a challenging task. Motivated by this spreading deformity in society, a lightweight and efficient model is proposed for the highly accurate classification of skin lesions. Dynamic-sized kernels are used in layers to obtain the best results, resulting in very few trainable parameters. Further, both ReLU and leakyReLU activation functions are purposefully used in the proposed model. The model accurately classified all of the classes of the HAM10000 dataset. The model achieved an overall accuracy of 97.85%, which is much better than multiple state-of-the-art heavy models. Further, our work is compared with some popular state-of-the-art and recent existing models.
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Affiliation(s)
- Theyazn H. H. Aldhyani
- Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
- Correspondence:
| | - Amit Verma
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
| | - Mosleh Hmoud Al-Adhaileh
- Deanship of E-Learning and Distance Education, King Faisal University, P.O. Box 4000, Al-Ahsa 31982, Saudi Arabia
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
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Ba W, Wu H, Chen WW, Wang SH, Zhang ZY, Wei XJ, Wang WJ, Yang L, Zhou DM, Zhuang YX, Zhong Q, Song ZG, Li CX. Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images. Eur J Cancer 2022; 169:156-165. [DOI: 10.1016/j.ejca.2022.04.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/20/2022] [Accepted: 04/07/2022] [Indexed: 12/24/2022]
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Vasanthakumari P, Romano RA, Rosa RGT, Salvio AG, Yakovlev V, Kurachi C, Hirshburg JM, Jo JA. Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:066002. [PMID: 35701871 PMCID: PMC9196925 DOI: 10.1117/1.jbo.27.6.066002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones. AIM To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy. APPROACH We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy. RESULTS Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered. CONCLUSIONS Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.
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Affiliation(s)
- Priyanka Vasanthakumari
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
| | - Renan A. Romano
- University of São Paulo, São Carlos Institute of Physics, São Paulo, Brazil
| | - Ramon G. T. Rosa
- University of São Paulo, São Carlos Institute of Physics, São Paulo, Brazil
| | - Ana G. Salvio
- Skin Department of Amaral Carvalho Hospital, São Paulo, Brazil
| | - Vladislav Yakovlev
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
| | - Cristina Kurachi
- University of São Paulo, São Carlos Institute of Physics, São Paulo, Brazil
| | - Jason M. Hirshburg
- University of Oklahoma Health Science Center, Department of Dermatology, Oklahoma City, Oklahoma, United States
| | - Javier A. Jo
- University of Oklahoma, School of Electrical and Computer Engineering, Norman, Oklahoma, United States
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Autonomous diagnosis of pediatric cutaneous vascular anomalies using a convolutional neural network. Int J Pediatr Otorhinolaryngol 2022; 156:111096. [PMID: 35334238 DOI: 10.1016/j.ijporl.2022.111096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/05/2022] [Accepted: 03/01/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVES Design and validate a novel handheld device for the autonomous diagnosis of pediatric vascular anomalies using a convolutional neural network (CNN). STUDY DESIGN Retrospective, cross-sectional study of medical images. Computer aided design and 3D printed manufacturing. METHODS We obtained a series of head and neck vascular anomaly images in pediatric patients from the database maintained in a large multidisciplinary vascular anomalies clinic. The database was supplemented with additional images from the internet. Four diagnostic classes were recognized in the dataset - infantile hemangioma, capillary malformation, venous malformation, and arterio-venous malformation. Our group designed and implemented a convolutional neural network to recognize the four classes of vascular anomalies as well as a fifth class consisting of none of the vascular anomalies. The system was based on the Inception-Resnet neural network using transfer learning. For deployment, we designed and built a compact, handheld device including a central processing unit, display subsystems, and control electronics. The device focuses upon and autonomously classifies pediatric vascular lesions. RESULTS The multiclass system distinguished the diagnostic categories with an overall accuracy of 84%. The inclusion of lesion metadata improved overall accuracy to 94%. Sensitivity ranged from 88% (venous malformation) to 100% (arterio-venous malformation and capillary malformation). CONCLUSIONS An easily deployed handheld device to autonomously diagnose pediatric skin lesions is feasible. Large training datasets and novel neural network architectures will be required for successful implementation.
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images. SENSORS 2022; 22:s22031134. [PMID: 35161878 PMCID: PMC8838143 DOI: 10.3390/s22031134] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/18/2022] [Accepted: 01/27/2022] [Indexed: 02/01/2023]
Abstract
Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.
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19
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TATL: Task Agnostic Transfer Learning for Skin Attributes Detection. Med Image Anal 2022; 78:102359. [DOI: 10.1016/j.media.2022.102359] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 11/20/2022]
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Xiang K, Peng L, Yang H, Li M, Cao Z, Jiang S, Qu G. A novel weight pruning strategy for light weight neural networks with application to the diagnosis of skin disease. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107707] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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21
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22
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Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:7192016. [PMID: 34621146 PMCID: PMC8457955 DOI: 10.1155/2021/7192016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/20/2021] [Accepted: 09/01/2021] [Indexed: 02/06/2023]
Abstract
The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC's early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist's experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.
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23
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Machine-learning algorithm to predict multidisciplinary team treatment recommendations in the management of basal cell carcinoma. Br J Cancer 2021; 126:562-568. [PMID: 34471257 DOI: 10.1038/s41416-021-01506-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 06/21/2021] [Accepted: 07/20/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Basal cell carcinoma (BCC) is the most common human cancer. Facial BCCs most commonly occur on the nose and the management of these lesions is particularly complex, given the functional and complex implications of treatment. Multidisciplinary team (MDT) meetings are routinely held to integrate expertise from dermatologists, surgeons, oncologists, radiologists, pathologists and allied health professionals. The aim of this research was to develop a supervised machine-learning algorithm to predict MDT recommendations for nasal BCC to potentially reduce MDT caseload, provide automatic decision support and permit data audit in a health service context. METHODS The study population included all consecutive patients who were discussed at skin cancer-specialised MDT (SSMDT) with a diagnosis of nasal BCC between January 1, 2015 and December 31, 2015. We conducted analyses for gender, age, anatomical location, histological subtype, tumour size, tumour recurrence, anticoagulation, pacemaker, immunosuppressants and therapeutic modalities (Mohs surgery, conventional excision or radiotherapy). We used S-statistic computing language to develop a supervised machine-learning algorithm. RESULTS We found that 37.5% of patients could be reliably predicted to be triaged to Mohs micrographic surgery (MMS), based on tumour location and age. Similarly, the choice of conventional treatment (surgical excision or radiotherapy) by the MDT could be reliably predicted based on the patient's age, tumour phenotype and lesion size. Accordingly, the algorithm reliably predicted the MDT decision outcome of 45.1% of nasal BCCs. CONCLUSIONS Our study suggests that the machine-learning approach is a potentially useful tool for predicting MDT decisions for MMS vs conventional surgery or radiotherapy for a significant group of patients. We suggest that utilising this algorithm gives the MDT more time to consider more complex patients, where multiple factors, including recurrence, financial costs and cosmetic outcome, contribute to the final decision, but cannot be reliably predicted to determine that outcome. This approach has the potential to reduce the burden and improve the efficiency of the specialist skin MDT and, in turn, improve patient care, reduce waiting times and reduce the financial burden. Such an algorithm would need to be updated regularly to take into account any changes in patient referral patterns, treatment options or local clinical expertise. CLINICAL TRIAL REGISTRATION lPLAS_20-21_A08.
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Soenksen LR, Kassis T, Conover ST, Marti-Fuster B, Birkenfeld JS, Tucker-Schwartz J, Naseem A, Stavert RR, Kim CC, Senna MM, Avilés-Izquierdo J, Collins JJ, Barzilay R, Gray ML. Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. Sci Transl Med 2021; 13:13/581/eabb3652. [PMID: 33597262 DOI: 10.1126/scitranslmed.abb3652] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/17/2020] [Accepted: 01/08/2021] [Indexed: 11/03/2022]
Abstract
A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to improved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatological patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.
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Affiliation(s)
- Luis R Soenksen
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA. .,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Cir, Boston, MA 02115, USA.,Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA.,MIT linQ, Massachusetts Institute of Technology Cambridge, MA 02148, USA
| | - Timothy Kassis
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA
| | - Susan T Conover
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Berta Marti-Fuster
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,MIT linQ, Massachusetts Institute of Technology Cambridge, MA 02148, USA
| | - Judith S Birkenfeld
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,MIT linQ, Massachusetts Institute of Technology Cambridge, MA 02148, USA
| | - Jason Tucker-Schwartz
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,MIT linQ, Massachusetts Institute of Technology Cambridge, MA 02148, USA
| | - Asif Naseem
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,MIT linQ, Massachusetts Institute of Technology Cambridge, MA 02148, USA
| | - Robert R Stavert
- Division of Dermatology, Cambridge Health Alliance, 1493 Cambridge Street, Cambridge, MA 02139, USA.,Department of Dermatology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, USA.,Department of Dermatology, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Caroline C Kim
- Pigmented Lesion Program, Newton Wellesley Dermatology Associates, 65 Walnut Street Suite 520 Wellesley Hills, MA 02481, USA.,Department of Dermatology, Tufts Medical Center, 260 Tremont Street Biewend Building, Boston, MA 02116, USA
| | - Maryanne M Senna
- Department of Dermatology, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.,Department of Dermatology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114
| | - José Avilés-Izquierdo
- Department of Dermatology, Hospital General Universitario Gregorio Marañón, Calle del Dr. Esquerdo 46, 28007 Madrid, Spain
| | - James J Collins
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Cir, Boston, MA 02115, USA.,Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge, MA 02148, USA
| | - Martha L Gray
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA.,Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA.,MIT linQ, Massachusetts Institute of Technology Cambridge, MA 02148, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge, MA 02148, USA
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Cheong KH, Tang KJW, Zhao X, Koh JEW, Faust O, Gururajan R, Ciaccio EJ, Rajinikanth V, Acharya UR. An automated skin melanoma detection system with melanoma-index based on entropy features. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9998379. [PMID: 34055044 PMCID: PMC8143893 DOI: 10.1155/2021/9998379] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/12/2021] [Accepted: 04/29/2021] [Indexed: 11/17/2022]
Abstract
In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.
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Pai VV, Pai RB. Artificial intelligence in dermatology and healthcare: An overview. Indian J Dermatol Venereol Leprol 2021; 87:457-467. [PMID: 34114421 DOI: 10.25259/ijdvl_518_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.
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Affiliation(s)
| | - Rohini Bhat Pai
- Department of Anaesthesiology, Goa Medical College, Bambolim, Goa, India
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28
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Mijwil MM. Skin cancer disease images classification using deep learning solutions. MULTIMEDIA TOOLS AND APPLICATIONS 2021. [DOI: 10.1007/s11042-021-10952-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 11/04/2020] [Accepted: 04/14/2021] [Indexed: 08/30/2023]
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A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5562801. [PMID: 33880368 PMCID: PMC8046537 DOI: 10.1155/2021/5562801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/17/2021] [Accepted: 03/26/2021] [Indexed: 11/17/2022]
Abstract
The segmentation of a skin lesion is regarded as very challenging because of the low contrast between the lesion and the surrounding skin, the existence of various artifacts, and different imaging acquisition conditions. The purpose of this study is to segment melanocytic skin lesions in dermoscopic and standard images by using a hybrid model combining a new hierarchical K-means and level set approach, called HK-LS. Although the level set method is usually sensitive to initial estimation, it is widely used in biomedical image segmentation because it can segment more complex images and does not require a large number of manually labelled images. The preprocessing step is used for the proposed model to be less sensitive to intensity inhomogeneity. The proposed method was evaluated on medical skin images from two publicly available datasets including the PH2 database and the Dermofit database. All skin lesions were segmented with high accuracies (>94%) and Dice coefficients (>0.91) of the ground truth on two databases. The quantitative experimental results reveal that the proposed method yielded significantly better results compared to other traditional level set models and has a certain advantage over the segmentation results of U-net in standard images. The proposed method had high clinical applicability for the segmentation of melanocytic skin lesions in dermoscopic and standard images.
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Sultana N. Predicting sun protection measures against skin diseases using machine learning approaches. J Cosmet Dermatol 2021; 21:758-769. [PMID: 33786953 DOI: 10.1111/jocd.14120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 03/16/2021] [Accepted: 03/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND The substantial growth rate of skin cancer has necessitated adequate protection from solar radiation. Consequently, analyzing sun protection practices is an imperative research area in dermatology and pharmacology. AIMS This paper aims to analyze public sun-protection manners in the Arabian Peninsula regions. METHODS A simple random survey was conducted to assess public sun protection manners. Artificial neural network (ANN) and support vector machine (SVM) were selected from several machine learning algorithms to create the models for predicting public sun protection measures based on the prediction accuracy. Model performances were evaluated based on several performance indicators depending on the confusion matrices and receiver operating characteristic curves. RESULTS 51% of the respondents have a low level, and 49% have a high level of sun protection practices. The results showed that the SVM performed considerably amended than the ANN for predicting the response. The relative importance of the predictors for the best predictive SVM model was also analyzed. The predictors are ranked as: the number of times having sunburnt >gender > use seat belt while driving/riding a vehicle >considers the UV index for personal sun exposure >income based on the expenses >sports/exercise activities >consciousness of the chance for having sunburnt on extended exposure to the sun >age > having any skin problem >nationality > skin type. CONCLUSION These identified significant predictors might be considered for developing an effective policy to increase public consciousness using proper protection from solar radiation's detrimental effect to rule out skin diseases.
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Affiliation(s)
- Nahid Sultana
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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31
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A Probabilistic-Based Deep Learning Model for Skin Lesion Segmentation. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as hairs, oil and bubbles, blood vessels, and skin lines. They also have variegated colors, low contrast, and irregular borders. Various computational approaches have been designed in the past for aiding in the detection and diagnosis of skin cancer diseases using skin lesion images. The existing techniques have been limited due to the interference of the aforementioned features of skin lesion. Recently, machine learning techniques, in particular the deep learning techniques have been used for the detection of skin cancer. However, they are still limited to the fuzzy and irregular borders of skin lesion images coupled with the low contrast that exists between the diseased lesion and healthy tissues. In this paper, we utilized a probabilistic model for the enhancement of a fully convolutional network-based deep learning system to analyze and segment skin lesion images. The probabilistic model employs an efficient mean-field approximate probabilistic inference approach with a fully connected conditional random field that utilizes a Gaussian kernel. The probabilistic model further performs a refinement of skin lesion borders. The whole framework is tested and evaluated on publicly available skin lesion image datasets of ISBI 2017 and PH2. The system achieved a better performance, having an accuracy of 98%.
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32
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Sevli O. A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05929-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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33
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An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11052145] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.
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Chaturvedi SS, Gupta K, Prasad PS. Skin Lesion Analyser: An Efficient Seven-Way Multi-class Skin Cancer Classification Using MobileNet. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2021. [DOI: 10.1007/978-981-15-3383-9_15] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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A A. A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images. J Biomed Phys Eng 2020; 10:801-806. [PMID: 33364218 PMCID: PMC7753251 DOI: 10.31661/jbpe.v0i0.2004-1107] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 06/06/2020] [Indexed: 06/01/2023]
Abstract
This work proposes a deep learning model for skin cancer detection from skin lesion images. In this analytic study, from HAM10000 dermoscopy image database, 3400 images were employed including melanoma and non-melanoma lesions. The images comprised 860 melanoma, 327 actinic keratoses and intraepithelial carcinoma (AKIEC), 513 basal cell carcinoma (BCC), 795 melanocytic nevi, 790 benign keratosis, and 115 dermatofibroma cases. A deep convolutional neural network was developed to classify the images into benign and malignant classes. A transfer learning method was leveraged with AlexNet as the pre-trained model. The proposed model takes the raw image as the input and automatically learns useful features from the image for classification. Therefore, it eliminates complex procedures of lesion segmentation and feature extraction. The proposed model achieved an area under the receiver operating characteristic (ROC) curve of 0.91. Using a confidence score threshold of 0.5, a classification accuracy of 84%, the sensitivity of 81%, and specificity of 88% was obtained. The user can change the confidence threshold to adjust sensitivity and specificity if desired. The results indicate the high potential of deep learning for the detection of skin cancer including melanoma and non-melanoma malignancies. The proposed approach can be deployed to assist dermatologists in skin cancer detection. Moreover, it can be applied in smartphones for self-diagnosis of malignant skin lesions. Hence, it may expedite cancer detection that is critical for effective treatment.
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Affiliation(s)
- Ameri A
- PhD, Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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36
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Vasconcelos CN, Vasconcelos BN. Experiments using deep learning for dermoscopy image analysis. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2017.11.005] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Bakheet S, Al-Hamadi A. Computer-Aided Diagnosis of Malignant Melanoma Using Gabor-Based Entropic Features and Multilevel Neural Networks. Diagnostics (Basel) 2020; 10:diagnostics10100822. [PMID: 33066517 PMCID: PMC7602255 DOI: 10.3390/diagnostics10100822] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/05/2020] [Accepted: 10/08/2020] [Indexed: 11/25/2022] Open
Abstract
The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discriminative descriptor for skin lesions is derived from local patterns of Gabor-based entropic features. The input skin image is first preprocessed by filtering and histogram equalization to reduce noise and enhance image quality. An automatic thresholding by the optimized formula of Otsu’s method is used for segmenting out lesion regions from the surrounding healthy skin regions. Then, an extensive set of optimized Gabor-based features is computed to characterize segmented skin lesions. Finally, the normalized features are fed into a trained Multilevel Neural Network to classify each pigmented skin lesion in a given dermoscopic image as benign or melanoma. The proposed detection methodology is successfully tested and validated on the public PH2 benchmark dataset using 5-cross-validation, achieving 97.5%, 100% and 96.87% in terms of accuracy, sensitivity and specificity, respectively, which demonstrate competitive performance compared with several recent state-of-the-art methods.
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Affiliation(s)
- Samy Bakheet
- Department of Information Technology, Faculty of Computers and Information, Sohag University, Sohag P.O. Box 82524, Egypt
- Correspondence:
| | - Ayoub Al-Hamadi
- Institute for Information Technology and Communications (IIKT), Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany;
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Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09865-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Liu G, Li C, Zhen H, Zhang Z, Sha Y. Identification of prognostic gene biomarkers for metastatic skin cancer using data mining. Biomed Rep 2020; 13:22-30. [PMID: 32494360 DOI: 10.3892/br.2020.1307] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/21/2020] [Indexed: 12/16/2022] Open
Abstract
Skin cancer is a common malignant tumor in China and throughout the world, and the rate of recurrence is considerably high, thus endangering the quality of life and health of patients, and increasing the economic burden and pressure to the families of those afflicted. Due to the limitations of traditional drug treatments, it is difficult to achieve the desired therapeutic effect of complete removal. However, targeted gene therapy may be a novel means of treating skin cancer, as the targeted nature of treatment may improve therapeutic outcomes. However, targeted gene therapy requires physicians to select the appropriate gene, which means suitable genetic biomarkers must be identified from complex genetic data. In the present study, the least absolute shrinkage and selection operator regression analysis method was used with 10-fold cross verification to reduce the dimensions of gene data in patients with skin cancer, and subsequently, 20 gene biomarkers were screened. A prognostic model was constructed using these 20 gene biomarkers, and the validity of the model was assessed using a training set and a verification set, which showed that the model performed well. Finally, gene function analysis of these 20 gene biomarkers was determined. Relevant studies were found to show that the genetic biomarkers identified in this paper may possess value for the follow-up clinical treatment of skin cancer.
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Affiliation(s)
- Gang Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P.R. China
| | - Chen Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P.R. China
| | - Haiyan Zhen
- The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, Gansu 730000, P.R. China
| | - Zhigang Zhang
- The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, Gansu 730000, P.R. China
| | - Yongzhong Sha
- School of Management, Lanzhou University, Lanzhou, Gansu 730000, P.R. China
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Wu H, Yin H, Chen H, Sun M, Liu X, Yu Y, Tang Y, Long H, Zhang B, Zhang J, Zhou Y, Li Y, Zhang G, Zhang P, Zhan Y, Liao J, Luo S, Xiao R, Su Y, Zhao J, Wang F, Zhang J, Zhang W, Zhang J, Lu Q. A deep learning, image based approach for automated diagnosis for inflammatory skin diseases. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:581. [PMID: 32566608 PMCID: PMC7290553 DOI: 10.21037/atm.2020.04.39] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background As the booming of deep learning era, especially the advances in convolutional neural networks (CNNs), CNNs have been applied in medicine fields like radiology and pathology. However, the application of CNNs in dermatology, which is also based on images, is very limited. Inflammatory skin diseases, such as psoriasis (Pso), eczema (Ecz), and atopic dermatitis (AD), are very easily to be mis-diagnosed in practice. Methods Based on the EfficientNet-b4 CNN algorithm, we developed an artificial intelligence dermatology diagnosis assistant (AIDDA) for Pso, Ecz & AD and healthy skins (HC). The proposed CNN model was trained based on 4,740 clinical images, and the performance was evaluated on experts-confirmed clinical images grouped into 3 different dermatologist-labelled diagnosis classifications (HC, Pso, Ecz & AD). Results The overall diagnosis accuracy of AIDDA is 95.80%±0.09%, with the sensitivity of 94.40%±0.12% and specificity 97.20%±0.06%. AIDDA showed accuracy for Pso is 89.46%, with sensitivity of 91.4% and specificity of 95.48%, and accuracy for AD & Ecz 92.57%, with sensitivity of 94.56% and specificity of 94.41%. Conclusions AIDDA is thus already achieving an impact in the diagnosis of inflammatory skin diseases, highlighting how deep learning network tools can help advance clinical practice.
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Affiliation(s)
- Haijing Wu
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Heng Yin
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | | | | | | | - Yizhou Yu
- DeepWise AI Lab, Beijing 100080, China
| | - Yang Tang
- Guanlan Networks (Hangzhou) Co., Ltd., Hangzhou 310000, China
| | - Hai Long
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Bo Zhang
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Jing Zhang
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Ying Zhou
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Yaping Li
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Guiyuing Zhang
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Peng Zhang
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Yi Zhan
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Jieyue Liao
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Shuaihantian Luo
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Rong Xiao
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Yuwen Su
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
| | - Juanjuan Zhao
- Guanlan Networks (Hangzhou) Co., Ltd., Hangzhou 310000, China
| | - Fei Wang
- Guanlan Networks (Hangzhou) Co., Ltd., Hangzhou 310000, China
| | - Jing Zhang
- Guanlan Networks (Hangzhou) Co., Ltd., Hangzhou 310000, China
| | - Wei Zhang
- Guanlan Networks (Hangzhou) Co., Ltd., Hangzhou 310000, China
| | - Jin Zhang
- Guanlan Networks (Hangzhou) Co., Ltd., Hangzhou 310000, China
| | - Qianjin Lu
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha 410011, China
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41
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Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072488] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged disease taxonomy, where available, to improve classification performance of these models. On DermNet we establish new state-of-the-art with 80% accuracy and 98% Area Under the Curve (AUC) for classification of 23 diseases. We also set precedence for classifying all 622 unique sub-classes in this dataset and achieved 67% accuracy and 98% AUC. On ISIC Archive we classified all 7 diseases with 93% average accuracy and 99% AUC. This study shows that Deep Learning has great potential to classify a vast array of skin diseases with near-human accuracy and far better reproducibility. It can have a promising role in practical real-time skin disease diagnosis by assisting physicians in large-scale screening using clinical or dermoscopic images.
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Kucharski D, Kleczek P, Jaworek-Korjakowska J, Dyduch G, Gorgon M. Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders. SENSORS 2020; 20:s20061546. [PMID: 32168748 PMCID: PMC7146382 DOI: 10.3390/s20061546] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 02/28/2020] [Accepted: 03/04/2020] [Indexed: 11/24/2022]
Abstract
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.
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Affiliation(s)
- Dariusz Kucharski
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
- Correspondence:
| | - Pawel Kleczek
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Joanna Jaworek-Korjakowska
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Grzegorz Dyduch
- Chair of Pathomorphology, Jagiellonian University Medical College, ul. Grzegorzecka 16, 31-531 Krakow, Poland
| | - Marek Gorgon
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
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Mabrouk MS, Sayed AY, Afifi HM, Sheha MA, Sharwy A. Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2020; 4:151-173. [DOI: 10.1007/s41666-020-00067-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 11/20/2019] [Accepted: 01/10/2020] [Indexed: 10/24/2022]
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44
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Kadampur MA, Al Riyaee S. Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2019.100282] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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Basu T, Engel-Wolf S, Menzer O. The Ethics of Machine Learning in Medical Sciences: Where Do We Stand Today? Indian J Dermatol 2020; 65:358-364. [PMID: 33165392 PMCID: PMC7640783 DOI: 10.4103/ijd.ijd_419_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Advances in Machine Learning and availability of state-of-the-art computational resources, along with digitized healthcare data, have set the stage for extensive application of artificial intelligence in the realm of diagnosis, prognosis, clinical decision support, personalized treatment options, drug development, and the field of biomedicine. Here, we discuss the application of Machine Learning algorithms in patient healthcare and dermatological domains along with the ethical complexities that are involved. In scientific studies, ethical challenges were initially not addressed proportionally (as assessed by keyword counts in PubMed) and just more recently (since 2016) this has started to improve. Few pioneering countries have created regulatory guidelines around how to respect matters of (1) privacy, (2) fairness, (3) accountability, (4) transparency and (5) conflict of interest when developing novel medical Machine Learning applications. While there is a strong promise of emerging medical applications to ultimately benefit both the patients and the medical practitioners, it is important to raise awareness on the five key ethical issues and incorporate them into medical practice in the near future.
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Affiliation(s)
- Treena Basu
- Department of Mathematics, Occidental College, Los Angeles, USA
| | - Sebastian Engel-Wolf
- Systems Biotechnology Group, Technical University of Munich, Boltzmannstr. 15, Garching, Germany
| | - Olaf Menzer
- Department of Geography, University of California, Santa Barbara, Newport Beach, CA, USA.,Technology Department, Retirement Solutions Division, Pacific Life, Newport Beach, CA, USA
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A comparative study of features selection for skin lesion detection from dermoscopic images. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/s13721-019-0209-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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47
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Pacheco AGC, Krohling RA. The impact of patient clinical information on automated skin cancer detection. Comput Biol Med 2019; 116:103545. [PMID: 31760271 DOI: 10.1016/j.compbiomed.2019.103545] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/14/2019] [Accepted: 11/14/2019] [Indexed: 01/08/2023]
Abstract
Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient clinical information, an important clue towards clinical diagnosis. In this work, we present an approach to fill this gap. First, we introduce a new dataset composed of clinical images, collected using smartphones, and clinical data related to the patient. Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep learning models to combine features from images and clinical data. Last, we carry out experiments to compare the models' performance with and without using this mechanism. The results present an improvement of approximately 7% in balanced accuracy when the aggregation method is applied. Overall, the impact of clinical data on models' performance is significant and shows the importance of including these features on automated skin cancer detection.
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Affiliation(s)
- Andre G C Pacheco
- Graduate Program in Computer Science, PPGI, UFES - Federal University of Espírito Santo, Av. Fernando Ferrari 514, Vitória CEP: 29060-270, Brazil.
| | - Renato A Krohling
- Graduate Program in Computer Science, PPGI, UFES - Federal University of Espírito Santo, Av. Fernando Ferrari 514, Vitória CEP: 29060-270, Brazil; Production Engineering Department, UFES - Federal University of Espírito Santo, Av. Fernando Ferrari 514, Vitória CEP: 29060-270, Brazil.
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Automated Lesion Segmentation and Quantitative Analysis of Nevus in Whole-Face Images. J Craniofac Surg 2019; 31:360-363. [PMID: 31725506 DOI: 10.1097/scs.0000000000006017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Nevus is very common; however, melanoma is slightly related to the deterioration of nevus because of its vulnerability to solarization, friction, aging, heredity, and other factors. Early diagnosis is essential for melanoma treatment, since patients have a high survival rate with early detection and treatment. Computer-aided diagnosis has been applied in the differential diagnosis of melanoma and benign nevi and achieved high accuracy, but it does not suit the screening of nevi because most studies are based on dermoscopy with a narrow field of vision and performed by professional doctors. Therefore, this study aimed to present the accuracy and effectiveness of our algorithm. METHODS Based on whole-face images of patients, the authors used logistic regression and the Newton method to detect the nevus region. Then, Python and OpenCV were employed to detect the lesion edge and compute the area of the regions. A multicenter clinical trial with a sample size of 600 was then conducted to evaluate the effectiveness of the algorithm. RESULTS The algorithm detected 2672 nevi from 600 patients, in which there were 195 patients of missed diagnosis and 310 patients of misdiagnosis. The Kappa value between 2 groups was 0.860 (>0.8). Paired t-test showed no significant difference between 2 groups' area results (P = 0.265, P > 0.05). CONCLUSION Within the limitations of this study, the authors demonstrated a high agreement between algorithm's detection and doctor's diagnosis. Our new algorithm has great effectiveness in nevus detection, edge segmentation, and area measurement.
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Fujisawa Y, Inoue S, Nakamura Y. The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers. Front Med (Lausanne) 2019; 6:191. [PMID: 31508420 PMCID: PMC6719629 DOI: 10.3389/fmed.2019.00191] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/13/2019] [Indexed: 11/13/2022] Open
Abstract
The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however, it is not practical for every single patient to receive intensive screening by dermatologists. To overcome this issue, many studies are ongoing to develop dermatologist-level, computer-aided diagnostics. Whereas, many systems that can classify dermoscopic images at this dermatologist-equivalent level have been reported, a much fewer number of systems that can classify conventional clinical images have been reported thus far. Recently, the introduction of deep-learning technology, a method that automatically extracts a set of representative features for further classification has dramatically improved classification efficacy. This new technology has the potential to improve the computer classification accuracy of conventional clinical images to the level of skilled dermatologists. In this review, this new technology and present development of computer-aided skin tumor classifiers will be summarized.
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Abstract
Many classification algorithms aim to minimize just their training error count; however, it is often desirable to minimize a more general cost metric, where distinct instances have different costs. In this paper, an instance-based cost-sensitive Bayesian consistent version of exponential loss function is proposed. Using the modified loss function, the derivation of instance-based cost-sensitive extensions of AdaBoost, RealBoost and GentleBoost are developed which are termed as ICSAdaBoost, ICSRealBoost and ICSGentleBoost, respectively. In this research, a new instance-based cost generation method is proposed instead of doing this expensive process by experts. Thus, each sample takes two cost values; a class cost and a sample cost. The first cost is equally assigned to all samples of each class while the second cost is generated according to the probability of each sample within its class probability density function. Experimental results of the proposed schemes imply 12% enhancement in terms of [Formula: see text]-measure and 13% on cost-per-sample over a variety of UCI datasets, compared to the state-of-the-art methods. The significant priority of the proposed method is supported by applying the pair of [Formula: see text]-tests to the results.
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Affiliation(s)
- Ensieh Sharifnia
- CSE & IT Dept., School of Electrical and Computer Engineering, Shiraz University, Campus#2, MollaSadra St., Shiraz 71348-51154, Iran
| | - Reza Boostani
- CSE & IT Dept., School of Electrical and Computer Engineering, Shiraz University, Campus#2, MollaSadra St., Shiraz 71348-51154, Iran
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