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Hosseinzadeh M, Hussain D, Zeki Mahmood FM, A. Alenizi F, Varzeghani AN, Asghari P, Darwesh A, Malik MH, Lee SW. A model for skin cancer using combination of ensemble learning and deep learning. PLoS One 2024; 19:e0301275. [PMID: 38820401 PMCID: PMC11142560 DOI: 10.1371/journal.pone.0301275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.
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Affiliation(s)
- Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
| | - Dildar Hussain
- Department of AI and Data Science, Sejong University, Seoul, Republic of Korea
| | | | - Farhan A. Alenizi
- Electrical Engineering Department, College of engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Parvaneh Asghari
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Aso Darwesh
- Department of Information Technology, University of Human Development, Sulaymaniyah, Kurdistan region of Iraq
| | - Mazhar Hussain Malik
- School of Computer Science and Creative Technologies College of Arts, Technology and Environment (CATE) University of the West of England Frenchay Campus, Coldharbour Lane Bristol, Bristol, United Kingdom
| | - Sang-Woong Lee
- Pattern Recognition and Machine Learning Lab, Gachon University, Seongnamdaero, Sujeonggu, Seongnam, Republic of Korea
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2
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Naeem A, Anees T. DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images. PLoS One 2024; 19:e0297667. [PMID: 38507348 PMCID: PMC10954125 DOI: 10.1371/journal.pone.0297667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/11/2024] [Indexed: 03/22/2024] Open
Abstract
Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.
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Affiliation(s)
- Ahmad Naeem
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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3
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Maurya A, Stanley RJ, Lama N, Nambisan AK, Patel G, Saeed D, Swinfard S, Smith C, Jagannathan S, Hagerty JR, Stoecker WV. Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:92-106. [PMID: 38343238 DOI: 10.1007/s10278-023-00924-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 03/02/2024]
Abstract
A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.
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Affiliation(s)
- Akanksha Maurya
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | - R Joe Stanley
- Missouri University of Science &Technology, Rolla, MO, 65209, USA.
| | - Norsang Lama
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | - Anand K Nambisan
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | | | | | | | - Colin Smith
- A.T. Still, University of Health Sciences, Kirksville, MO, USA
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Monica KM, Shreeharsha J, Falkowski-Gilski P, Falkowska-Gilska B, Awasthy M, Phadke R. Melanoma skin cancer detection using mask-RCNN with modified GRU model. Front Physiol 2024; 14:1324042. [PMID: 38292449 PMCID: PMC10825805 DOI: 10.3389/fphys.2023.1324042] [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: 10/24/2023] [Accepted: 12/18/2023] [Indexed: 02/01/2024] Open
Abstract
Introduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic images are acquired from two online benchmark datasets: International Skin Imaging Collaboration (ISIC) 2020 and Human against Machine (HAM) 10000. Subsequently, a normalization technique is employed on the dermoscopic images to decrease noise impact, outliers, and variations in the pixels. Furthermore, cancerous regions in the pre-processed images are segmented utilizing the mask-faster Region based Convolutional Neural Network (RCNN) model. The mask-RCNN model offers precise pixellevel segmentation by accurately delineating object boundaries. From the partitioned cancerous regions, discriminative feature vectors are extracted by applying three pre-trained CNN models, namely ResNeXt101, Xception, and InceptionV3. These feature vectors are passed into the modified Gated Recurrent Unit (GRU) model for MSC classification. In the modified GRU model, a swish-Rectified Linear Unit (ReLU) activation function is incorporated that efficiently stabilizes the learning process with better convergence rate during training. Results and discussion: The empirical investigation demonstrate that the modified GRU model attained an accuracy of 99.95% and 99.98% on the ISIC 2020 and HAM 10000 datasets, where the obtained results surpass the conventional detection models.
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Affiliation(s)
- K. M. Monica
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - J. Shreeharsha
- Department of Computer Science and Engineering, Rao Bahadur Y. Mahabaleswarappa Engineering College, Ballari, Karnataka, India
| | | | | | - Mohan Awasthy
- Department of Engineering and Technology, Bharati Vidyapeeth Peeth Deemed to be University, Navi Mumbai, Maharashtra, India
| | - Rekha Phadke
- Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India
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Viknesh CK, Kumar PN, Seetharaman R, Anitha D. Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique. Diagnostics (Basel) 2023; 13:3313. [PMID: 37958209 PMCID: PMC10649387 DOI: 10.3390/diagnostics13213313] [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: 08/22/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023] Open
Abstract
Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.
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Affiliation(s)
- Chandran Kaushik Viknesh
- Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India; (P.N.K.); (R.S.)
| | - Palanisamy Nirmal Kumar
- Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India; (P.N.K.); (R.S.)
| | - Ramasamy Seetharaman
- Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India; (P.N.K.); (R.S.)
| | - Devasahayam Anitha
- Department of Science and Humanities, Karpagam Institute of Technology, Coimbatore 641105, India;
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Radhika V, Chandana BS. MSCDNet-based multi-class classification of skin cancer using dermoscopy images. PeerJ Comput Sci 2023; 9:e1520. [PMID: 37705664 PMCID: PMC10495937 DOI: 10.7717/peerj-cs.1520] [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: 12/12/2022] [Accepted: 07/18/2023] [Indexed: 09/15/2023]
Abstract
Background Skin cancer is a life-threatening disease, and early detection of skin cancer improves the chances of recovery. Skin cancer detection based on deep learning algorithms has recently grown popular. In this research, a new deep learning-based network model for the multiple skin cancer classification including melanoma, benign keratosis, melanocytic nevi, and basal cell carcinoma is presented. We propose an automatic Multi-class Skin Cancer Detection Network (MSCD-Net) model in this research. Methods The study proposes an efficient semantic segmentation deep learning model "DenseUNet" for skin lesion segmentation. The semantic skin lesions are segmented by using the DenseUNet model with a substantially deeper network and fewer trainable parameters. Some of the most relevant features are selected using Binary Dragonfly Algorithm (BDA). SqueezeNet-based classification can be made in the selected features. Results The performance of the proposed model is evaluated using the ISIC 2019 dataset. The DenseNet connections and UNet links are used by the proposed DenseUNet segmentation model, which produces low-level features and provides better segmentation results. The performance results of the proposed MSCD-Net model are superior to previous research in terms of effectiveness and efficiency on the standard ISIC 2019 dataset.
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Affiliation(s)
| | - B. Sai Chandana
- School of Computer Science Engineering, VIT-AP University, Amaravathi, India
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Abbas Q, Daadaa Y, Rashid U, Ibrahim MEA. Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification. Diagnostics (Basel) 2023; 13:2531. [PMID: 37568894 PMCID: PMC10417387 DOI: 10.3390/diagnostics13152531] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/22/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community.
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Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (Y.D.); (M.E.A.I.)
| | - Yassine Daadaa
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (Y.D.); (M.E.A.I.)
| | - Umer Rashid
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
| | - Mostafa E. A. Ibrahim
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (Y.D.); (M.E.A.I.)
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Qalubia, Benha 13518, Egypt
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8
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Mehmood A, Gulzar Y, Ilyas QM, Jabbari A, Ahmad M, Iqbal S. SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions. Cancers (Basel) 2023; 15:3604. [PMID: 37509267 PMCID: PMC10377736 DOI: 10.3390/cancers15143604] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/05/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Skin cancer is a major public health concern around the world. Skin cancer identification is critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists in skin cancer diagnosis. This study proposes SBXception: a shallower and broader variant of the Xception network. It uses Xception as the base model for skin cancer classification and increases its performance by reducing the depth and expanding the breadth of the architecture. We used the HAM10000 dataset, which contains 10,015 dermatoscopic images of skin lesions classified into seven categories, for training and testing the proposed model. Using the HAM10000 dataset, we fine-tuned the new model and reached an accuracy of 96.97% on a holdout test set. SBXception also achieved significant performance enhancement with 54.27% fewer training parameters and reduced training time compared to the base model. Our findings show that reducing and expanding the Xception model architecture can greatly improve its performance in skin cancer categorization.
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Affiliation(s)
- Abid Mehmood
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Yonis Gulzar
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Qazi Mudassar Ilyas
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Abdoh Jabbari
- College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia
| | - Muneer Ahmad
- Department of Human and Digital Interface, Woosong University, Daejeon 34606, Republic of Korea
| | - Sajid Iqbal
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
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9
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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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10
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Balaji P, Hung BT, Chakrabarti P, Chakrabarti T, Elngar AA, Aluvalu R. A novel artificial intelligence-based predictive analytics technique to detect skin cancer. PeerJ Comput Sci 2023; 9:e1387. [PMID: 37346565 PMCID: PMC10280503 DOI: 10.7717/peerj-cs.1387] [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: 02/07/2023] [Accepted: 04/20/2023] [Indexed: 06/23/2023]
Abstract
One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system's training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated using a Python program, and the current research's parameters are assessed against those of earlier studies. The results demonstrate that, compared to other models, the new research model produces better accuracy for predicting skin cancer by segmentation.
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Affiliation(s)
- Prasanalakshmi Balaji
- Data Science Laboratory, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam
| | - Bui Thanh Hung
- Data Science Laboratory, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam
| | | | | | - Ahmed A. Elngar
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Rajanikanth Aluvalu
- Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India
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Tahir M, Naeem A, Malik H, Tanveer J, Naqvi RA, Lee SW. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers (Basel) 2023; 15:cancers15072179. [PMID: 37046840 PMCID: PMC10093058 DOI: 10.3390/cancers15072179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.
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Affiliation(s)
- Maryam Tahir
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan Sub Campus, Multan 60000, Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Hassaan Malik
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan Sub Campus, Multan 60000, Pakistan
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Jawad Tanveer
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Rizwan Ali Naqvi
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung-Won Lee
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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12
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Cai ZH, Zhang Q, Fu ZW, Fingerhut A, Tan JW, Zang L, Dong F, Li SC, Wang SL, Ma JJ. Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis. World J Gastroenterol 2023; 29:536-548. [PMID: 36688017 PMCID: PMC9850934 DOI: 10.3748/wjg.v29.i3.536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/29/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Multiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis. AIM To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI). METHODS We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (n = 260) and testing set (n = 68). Binary logistic regression was adopted to create a clinical model using six factors. The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed. Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks. Sensitivity, specificity, accuracy, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC) was calculated for each model. RESULTS The prevalence of ≥ 3 linear stapler cartridges was 17.7% (58/328). The prevalence of AL was statistically significantly higher in patients with ≥ 3 cartridges compared to those with ≤ 2 cartridges (25.0% vs 11.8%, P = 0.018). Preoperative carcinoembryonic antigen level > 5 ng/mL (OR = 2.11, 95%CI 1.08-4.12, P = 0.028) and tumor size ≥ 5 cm (OR = 3.57, 95%CI 1.61-7.89, P = 0.002) were recognized as independent risk factors for use of ≥ 3 linear stapler cartridges. Diagnostic performance was better with the integrated model (accuracy = 94.1%, PPV = 87.5%, and AUC = 0.88) compared with the clinical model (accuracy = 86.7%, PPV = 38.9%, and AUC = 0.72) and the image model (accuracy = 91.2%, PPV = 83.3%, and AUC = 0.81). CONCLUSION MRI-based deep learning model can predict the use of ≥ 3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery. This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for ≥ 3 linear stapler cartridges.
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Affiliation(s)
- Zheng-Hao Cai
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai Minimally Invasive Surgery Center, Shanghai 200025, China
| | - Qun Zhang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China
| | - Zhan-Wei Fu
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Abraham Fingerhut
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jing-Wen Tan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lu Zang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Feng Dong
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shu-Chun Li
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shi-Lin Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China
| | - Jun-Jun Ma
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Zafar M, Sharif MI, Sharif MI, Kadry S, Bukhari SAC, Rauf HT. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. LIFE (BASEL, SWITZERLAND) 2023; 13:life13010146. [PMID: 36676093 PMCID: PMC9864434 DOI: 10.3390/life13010146] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 12/25/2022] [Accepted: 12/28/2022] [Indexed: 01/06/2023]
Abstract
The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.
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Affiliation(s)
- Mehwish Zafar
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
| | - Muhammad Irfan Sharif
- Department of Computer Science, University of Education, Jauharabad Campus, Khushāb 41200, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
- Correspondence:
| | - Syed Ahmad Chan Bukhari
- Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, Queens, NY 11439, USA
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
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14
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Ghosh P, Azam S, Quadir R, Karim A, Shamrat FMJM, Bhowmik SK, Jonkman M, Hasib KM, Ahmed K. SkinNet-16: A deep learning approach to identify benign and malignant skin lesions. Front Oncol 2022; 12:931141. [PMID: 36003775 PMCID: PMC9395205 DOI: 10.3389/fonc.2022.931141] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method. Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.
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Affiliation(s)
- Pronab Ghosh
- Department of Computer Science (CS), Lakehead University, Thunder Bay, ON, Canada
| | - Sami Azam
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
- *Correspondence: Sami Azam,
| | - Ryana Quadir
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Asif Karim
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
| | - F. M. Javed Mehedi Shamrat
- Department of Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
| | - Shohag Kumar Bhowmik
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Mirjam Jonkman
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
| | - Khan Md. Hasib
- Department of Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
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15
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Naeem A, Anees T, Fiza M, Naqvi RA, Lee SW. SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155652. [PMID: 35957209 PMCID: PMC9371071 DOI: 10.3390/s22155652] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/19/2022] [Accepted: 07/25/2022] [Indexed: 05/27/2023]
Abstract
Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.
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Affiliation(s)
- Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan;
| | - Tayyaba Anees
- Department of Software Engineering, University of Management and Technology, Lahore 54000, Pakistan;
| | - Makhmoor Fiza
- Department of Management Sciences and Technology, Begum Nusrat Bhutto Women University, Sukkur 65200, Pakistan;
| | - Rizwan Ali Naqvi
- Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea
| | - Seung-Won Lee
- Department of Data Science, College of Software Convergence, Sejong University, Seoul 05006, Korea
- School of Medicine, Sungkyunkwan University, Suwon 16419, Korea
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16
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Elashiri MA, Rajesh A, Nath Pandey S, Kumar Shukla S, Urooj S, Lay-Ekuakille A. Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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17
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Ai Z, Huang X, Feng J, Wang H, Tao Y, Zeng F, Lu Y. FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network. Front Neuroinform 2022; 16:876927. [PMID: 35784186 PMCID: PMC9243322 DOI: 10.3389/fninf.2022.876927] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/04/2022] [Indexed: 01/31/2023] Open
Abstract
Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.
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Affiliation(s)
- Zhuang Ai
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China
| | - Xuan Huang
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Medical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jing Feng
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hui Wang
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yong Tao
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Sichuan, China
| | - Yaping Lu
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China
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18
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Abstract
Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s survival rate. It necessitates the development of a computer-assisted diagnostic support system. This research proposes a novel deep transfer learning model for melanoma classification using MobileNetV2. The MobileNetV2 is a deep convolutional neural network that classifies the sample skin lesions as malignant or benign. The performance of the proposed deep learning model is evaluated using the ISIC 2020 dataset. The dataset contains less than 2% malignant samples, raising the class imbalance. Various data augmentation techniques were applied to tackle the class imbalance issue and add diversity to the dataset. The experimental results demonstrate that the proposed deep learning technique outperforms state-of-the-art deep learning techniques in terms of accuracy and computational cost.
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19
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Mijwil MM, Aggarwal K. A diagnostic testing for people with appendicitis using machine learning techniques. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:7011-7023. [PMID: 35095329 PMCID: PMC8785023 DOI: 10.1007/s11042-022-11939-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/09/2021] [Accepted: 01/03/2022] [Indexed: 05/07/2023]
Abstract
Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.
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Affiliation(s)
- Maad M. Mijwil
- Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
| | - Karan Aggarwal
- Electronics and Communication Engineering Department, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India
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20
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Rajeshwari J, Sughasiny M. Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique. AIMS ELECTRONICS AND ELECTRICAL ENGINEERING 2022. [DOI: 10.3934/electreng.2023005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
<abstract>
<p>Skin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis of skin lesions is not insignificant, slightly worse than that of dermatologists, and costly and time-consuming. Various machine learning algorithms have been used to detect the severity of the disease diagnosis. But it is more complex when detecting the disease. To overcome these issues, a modified Probabilistic Neural Network (MPNN) classifier has been proposed to determine the severity of skin cancer. The proposed method contains two phases such as training and testing the data. The collected features from the data of infected people are used as input to the modified PNN classifier in the current model. The neural network is also trained using Spider Monkey Optimization (SMO) approach. For analyzing the severity level, the classifier predicts four classes. The degree of skin cancer is determined depending on classifications. According to findings, the system achieved a 0.10% False Positive Rate (FPR), 0.03% error and 0.98% accuracy, while previous methods like KNN, NB, RF and SVM have accuracies of 0.90%, 0.70%, 0.803% and 0.86% correspondingly, which is lesser than the proposed approach.</p>
</abstract>
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