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Maheswari M, Ahamed Ayoobkhan MU, Shirley CP, Lakshmi TRV. Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images. Med Biol Eng Comput 2024:10.1007/s11517-024-03106-y. [PMID: 38833025 DOI: 10.1007/s11517-024-03106-y] [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: 08/10/2023] [Accepted: 04/20/2024] [Indexed: 06/06/2024]
Abstract
Melanoma is an uncommon and dangerous type of skin cancer. Dermoscopic imaging aids skilled dermatologists in detection, yet the nuances between melanoma and non-melanoma conditions complicate diagnosis. Early identification of melanoma is vital for successful treatment, but manual diagnosis is time-consuming and requires a dermatologist with training. To overcome this issue, this article proposes an Optimized Attention-Induced Multihead Convolutional Neural Network with EfficientNetV2-fostered melanoma classification using dermoscopic images (AIMCNN-ENetV2-MC). The input pictures are extracted from the dermoscopic images dataset. Adaptive Distorted Gaussian Matched Filter (ADGMF) is used to remove the noise and maximize the superiority of skin dermoscopic images. These pre-processed images are fed to AIMCNN. The AIMCNN-ENetV2 classifies acral melanoma and benign nevus. Boosted Chimp Optimization Algorithm (BCOA) optimizes the AIMCNN-ENetV2 classifier for accurate classification. The proposed AIMCNN-ENetV2-MC is implemented using Python. The proposed approach attains an outstanding overall accuracy of 98.75%, less computation time of 98 s compared with the existing models.
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Affiliation(s)
- M Maheswari
- Department of Information Technology, DMI College of Engineering, Chennai, Tamil Nadu, India.
| | | | - C P Shirley
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| | - T R Vijaya Lakshmi
- Department of Electronics and Communication Engineering, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, India
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Lin Z, Shen H, Liu X, Ma W, Wang M, Ruan J, Yu H, Ma S, Sun X. Recent advances of artificial intelligence in melanoma clinical practice. Melanoma Res 2023; 33:454-461. [PMID: 37696256 DOI: 10.1097/cmr.0000000000000922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Skin melanoma is a lethal cancer. The incidence of melanoma is increasing rapidly in all regions of the world. Despite significant breakthroughs in melanoma treatment in recent years, precise diagnosis of melanoma is still a challenge in some cases. Even specialized physicians may need time and effort to make accurate judgments. As artificial intelligence (AI) technology advances into medical practice, it may bring new solutions to this problem based on its efficiency, accuracy, and speed. This paper summarizes the recent progress of AI in melanoma-related applications, including melanoma diagnosis and classification, the discovery of new medication, guiding treatment, and prognostic assessment. The paper also compares the effectiveness of various algorithms in melanoma application and suggests future research directions for AI in melanoma clinical practice.
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Affiliation(s)
- Zijun Lin
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
| | - Haoyan Shen
- School of Biomedical Engineering, Guangdong Medical University
| | - Xinguang Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
| | - Wanrui Ma
- Department of General Medicine, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan
| | - Mingfa Wang
- Department of Pathology, The Second Affiliated Hospital of Hainan Medical University, Haikou
| | - Jie Ruan
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
| | - Hongbin Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Chinese American Tumor Institute, Guangdong Medical University, Dongguan, China
| | - Sha Ma
- School of Biomedical Engineering, Guangdong Medical University
| | - Xuerong Sun
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
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A Survey on Computer-Aided Intelligent Methods to Identify and Classify Skin Cancer. INFORMATICS 2022. [DOI: 10.3390/informatics9040099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing precise findings before the critical stage. The computer-aided diagnostic process includes preprocessing, segmentation, feature extraction, and classification. This study discusses the advantages and disadvantages of various computer-aided algorithms. It also discusses the current approaches, problems, and various types of datasets for skin images. Information about possible future works is also highlighted in this paper. The inferences derived from this survey will be useful for researchers carrying out research in skin cancer image analysis.
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Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatol Ther (Heidelb) 2022; 12:2637-2651. [PMID: 36306100 PMCID: PMC9674813 DOI: 10.1007/s13555-022-00833-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/07/2022] [Indexed: 01/07/2023] Open
Abstract
Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.
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Affiliation(s)
- Konstantinos Liopyris
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
- Dermatology Department, Memorial Sloan Kettering Cancer Center, New York, NY, 10021, USA
| | - Stamatios Gregoriou
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece.
| | - Julia Dias
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
| | - Alexandros J Stratigos
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
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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: 13] [Impact Index Per Article: 6.5] [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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Wei Z, Wu X, Tong W, Zhang S, Yang X, Tian J, Hui H. Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network. BIOMEDICAL OPTICS EXPRESS 2022; 13:1292-1311. [PMID: 35414974 PMCID: PMC8973169 DOI: 10.1364/boe.448838] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/15/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
Stripe artifacts can deteriorate the quality of light sheet fluorescence microscopy (LSFM) images. Owing to the inhomogeneous, high-absorption, or scattering objects located in the excitation light path, stripe artifacts are generated in LSFM images in various directions and types, such as horizontal, anisotropic, or multidirectional anisotropic. These artifacts severely degrade the quality of LSFM images. To address this issue, we proposed a new deep-learning-based approach for the elimination of stripe artifacts. This method utilizes an encoder-decoder structure of UNet integrated with residual blocks and attention modules between successive convolutional layers. Our attention module was implemented in the residual blocks to learn useful features and suppress the residual features. The proposed network was trained and validated by generating three different degradation datasets with different types of stripe artifacts in LSFM images. Our method can effectively remove different stripes in generated and actual LSFM images distorted by stripe artifacts. Besides, quantitative analysis and extensive comparison results demonstrated that our method performs the best compared with classical image-based processing algorithms and other powerful deep-learning-based destriping methods for all three generated datasets. Thus, our method has tremendous application prospects to LSFM, and its use can be easily extended to images reconstructed by other modalities affected by the presence of stripe artifacts.
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Affiliation(s)
- Zechen Wei
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiangjun Wu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100083, China
| | - Wei Tong
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing 100853, China
| | - Suhui Zhang
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing 100853, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- Zhuhai Precision Medical Center, Zhuhai People's Hospital, affiliated with Jinan University, Zhuhai 519000, China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
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