Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

Shi, Wang; Hamian, Melika

doi:10.1155/2021/9528664

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…Specifically, AlexNet and DenseNet201 were explored to overcome the skin lesion detection challenges. Recently, YOLO-based approaches have been adapted for combined localization and classification tasks [25,26]. Table 2 summarizes the state-of-the-art studies that exploited deep learning-based approaches for classification purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks

AlSadhan,

Alamri,

Ben Ismail

et al. 2024

Cancers

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The incidence of skin cancer is rising globally, posing a significant public health threat. An early and accurate diagnosis is crucial for patient prognoses. However, discriminating between malignant melanoma and benign lesions, such as nevi and keratoses, remains a challenging task due to their visual similarities. Image-based recognition systems offer a promising solution to aid dermatologists and potentially reduce unnecessary biopsies. This research investigated the performance of four unified convolutional neural networks, namely, YOLOv3, YOLOv4, YOLOv5, and YOLOv7, in classifying skin lesions. Each model was trained on a benchmark dataset, and the obtained performances were compared based on lesion localization, classification accuracy, and inference time. In particular, YOLOv7 achieved superior performance with an Intersection over Union (IoU) of 86.3%, a mean Average Precision (mAP) of 75.4%, an F1-measure of 80%, and an inference time of 0.32 s per image. These findings demonstrated the potential of YOLOv7 as a valuable tool for aiding dermatologists in early skin cancer diagnosis and potentially reducing unnecessary biopsies.

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Section: Literature Reviewmentioning

confidence: 99%

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks

AlSadhan,

Alamri,

Ben Ismail

et al. 2024

Cancers

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“…They are well-known for their capacity to identify complicated trends in data that are highly dimensional and have made substantial contributions to the field of deep learning research. To achieve greater efficacy in many terms, a modified version of the recently established thermal exchange optimization (dTEO) technique has been used to carry out the optimization process in DBN by Wang [91] and achieved highly efficient results. Through a comparison of the segmented outcomes with reality, significant performance metrics were developed.…”

Section: Artificial Neural Network In Skin Lesion Detectionmentioning

confidence: 99%

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Hussain,

Toscano

2024

Symmetry

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Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.

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“…Poor camera quality, a minimal user interface in photography, and environmental conditions can all lead to distorted digital skin images. However, important visual information is sometimes lost in the cases above, making processing too difficult [31]. All of these factors can reduce the contrast in a picture.…”

Section: Image Pre-processingmentioning

confidence: 99%

Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework

Ozsahin

Mustapha²,

Uzun

et al. 2023

Diagnostics

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Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that a rapid, accurate, and reliable diagnosis may help reduce the risk of a future outbreak. The presence of skin lesions is one of the most prominent symptoms of the disease. However, this symptom is also peculiar to chickenpox. The resemblance in skin lesions in the human subject may disrupt effective diagnosis and, as a result, lead to misdiagnosis. Such misdiagnosis can lead to the further spread of the disease as it is a communicable disease and can eventually result in an outbreak. As deep learning (DL) algorithms have recently been regarded as a promising technique in medical fields, we have been attempting to integrate a well-trained DL algorithm to assist in the early detection and classification of skin lesions in human subjects. This study used two open-sourced digital skin images for monkeypox and chickenpox. A two-dimensional convolutional neural network (CNN) consisting of four convolutional layers was applied. Afterward, three MaxPooling layers were used after the second, third, and fourth convolutional layers. Finally, we evaluated the performance of our proposed model with state-of-the-art deep-learning models for skin lesions detection. Our proposed CNN model outperformed all DL models with a test accuracy of 99.60%. In addition, a weighted average precision, recall, F1 score of 99.00% was recorded. Subsequently, Alex Net outperformed other pre-trained models with an accuracy of 98.00%. The VGGNet consisting of VGG16 and VGG19 performed least well with an accuracy of 80.00%. Due to the uniqueness of the proposed model and image augmentation techniques applied, the proposed CNN model is generalized and avoids over-fitting. This model would be helpful for the rapid and accurate detection of monkeypox using digital skin images of patients with suspected monkeypox.

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Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

Cited by 15 publications

References 42 publications

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework

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