A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

Ahmad, Naveed; Shah, Jamal Hussain; Khan, Muhammad Attique; Baili, Jamel; Ansari, Ghulam Jillani; Tariq, Usman; Kim, Ye Jin; Cha, Jaehyuk

doi:10.3389/fonc.2023.1151257

Cited by 9 publications

(3 citation statements)

References 60 publications

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“…As for recent works, ref. [51] introduced Xception and ShuffleNet models using transfer learning and utilized butterfly optimization to improve feature selection. Ref.…”

Section: Classificationmentioning

confidence: 99%

An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis

Chae

Kim

2023

Applied Sciences

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A significant amount of research has investigated automating medical diagnosis using deep learning. However, because medical data are collected through diagnostic tests, deep learning methods used in existing studies have had a disadvantage in that the number of training samples is insufficient and the labeling cost is high. Training approaches considering the common characteristics of medical images are needed. Therefore, in this study, we investigated approaches to overcome the lack of data for representative medical imaging tasks using transfer learning technologies. The tasks were divided into image classification, object detection, and segmentation, commonly needed functions in medical image analyses. We proposed transfer learning approaches suitable for each task that can be applied when there are little medical image data available. These approaches were experimentally validated in the following applications that share similar issues of lacking data: cervical cancer classification (image classification), skin lesion detection and classification (object detection and classification), and pressure ulcer segmentation (segmentation). We also proposed multi-task learning and ensemble learning that can be applied to these applications. Finally, the approaches were compared with state-of-the-art results. In cervical cancer analysis, the performance was improved by 5.4% in sensitivity. Skin lesion classification showed improvement in accuracy of 8.7%, precision of 28.3%, and sensitivity of 39.7%. Finally, pressure ulcer segmentation improved in accuracy by 1.2%, intersection over union by 16.9%, and Dice similarity coefficient by 3.5%.

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“…As for recent works, ref. [51] introduced Xception and ShuffleNet models using transfer learning and utilized butterfly optimization to improve feature selection. Ref.…”

Section: Classificationmentioning

confidence: 99%

An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis

Chae

Kim

2023

Applied Sciences

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“…The presence of artifacts such as hairs, bubbles, and noise can be the reason for inapt feature extraction that later decreases classification accuracy. Classifying several skin malignant tumors into an exact class is a complicated and challenging task due to the maximum similarity among various lesions [21]. The chance of a higher sample class increases when the dataset object classes are unequal.…”

Section: Introductionmentioning

confidence: 99%

Multiclass skin lesion classification using deep learning networks optimal information fusion

Khan,

Hamza,

Shabaz

et al. 2024

Discov Appl Sci

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A serious, all-encompassing, and deadly cancer that affects every part of the body is skin cancer. The most prevalent causes of skin lesions are UV radiation, which can damage human skin, and moles. If skin cancer is discovered early, it may be adequately treated. In order to diagnose skin lesions with less effort, dermatologists are increasingly turning to machine learning (ML) techniques and computer-aided diagnostic (CAD) systems. This paper proposes a computerized method for multiclass lesion classification using a fusion of optimal deep-learning model features. The dataset used in this work, ISIC2018, is imbalanced; therefore, augmentation is performed based on a few mathematical operations. After that, two pre-trained deep learning models (DarkNet-19 and MobileNet-V2) have been fine-tuned and trained on the selected dataset. After training, features are extracted from the average pool layer and optimized using a hybrid firefly optimization technique. The selected features are fused in two ways: (i) original serial approach and (ii) proposed threshold approach. Machine learning classifiers are used to classify the chosen features at the end. Using the ISIC2018 dataset, the experimental procedure produced an accuracy of 89.0%. Whereas, 87.34, 87.57, and 87.45 are sensitivity, precision, and F1 score respectively. At the end, comparison is also conducted with recent techniques, and it shows the proposed method shows improved accuracy along with other performance measures.

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“…They include metadata related to clinical diagnosis, lesion type, and body location, among other factors. Their open access and the diversity of data they offer make them highly valuable to the scientific community, in dermatological research, and in the development of artificial intelligence tools for the diagnosis of skin diseases as a significant complement to expert diagnosis [87][88][89][90][91]. Additionally, the PH2 dataset consists of a recompilation of 200 images, focusing on a local objective rather than being broadly applicable to other case studies [69,74].…”

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confidence: 99%

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

Hermosilla,

Soto,

Vega

et al. 2024

Diagnostics

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In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

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A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

Cited by 9 publications

References 60 publications

An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis

An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis

Multiclass skin lesion classification using deep learning networks optimal information fusion

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

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