An Interpretable Deep Learning Model for Skin Lesion Classification

Jha, Avinash Kumar; Ananthanarayana, V. S.

doi:10.1007/978-3-031-37940-6_44

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…For instance, Shah Junayed et al [9] introduced AcneNet, a deep Convolutional Neural Network (CNN)-based classification approach for acne classes, demonstrating the potential for automated diagnosis in dermatology. Srinivasu et al [10], Wu et al [11], and Xiang et al [12] applied deep learning techniques for skin disease classification, utilizing neural networks like MobileNet V2, CNNs, and LSTM for improved diagnostic accuracy. Zhao et al [13] and Liu et al [14] focused on interpretable skin lesion classification using novel Convolutional Neural Network (CNN) algorithms and mole detection and segmentation software for mobile phone skin images.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Sankar,

Chaturvedi,

Nayan

et al. 2024

BioMedInformatics

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Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Sankar,

Chaturvedi,

Nayan

et al. 2024

BioMedInformatics

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show abstract

“…Loganathan, et al [2] suggested a new DCNN for classifying malignant melanoma (skin cancer). The recommended method comprises pre-processing, enhanced fuzzy clustering for melanoma detection, and enhanced deep convolutional neural networks (E-DCNN) for categorization of dermoscopy images.…”

Section: Chaurasia and Palmentioning

confidence: 99%

“…www.ijacsa.thesai.org SVM was developed from the theory of Structural Risk Minimization. In SVM, two essential parameters are to focus on, namely Gaussian width and the regularization parameters [2]. Boosted SVM is applied over the training dataset, and the weights are updated until convergence if the error rate exceeds 0.5.…”

Section: A Melanoma Detection Using Boosted Svmmentioning

confidence: 99%

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Skin Cancer Image Detection and Classification by CNN based Ensemble Learning

Alshawi,

Musawi

2023

IJACSA

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Melanoma is accounted as a rare skin cancer responsible for a huge mortality rate. However, various imaging tests can be used to detect the metastatic spread of disease with a primary diagnosis or on clinical suspicion. Focus on melanoma detection, irrespective of its unusual occurrence, is that it is often misdiagnosed for other skin malignancies leading to medical negligence. Sometimes melanoma is detected only when the metastasis has entered the bloodstream or lymph nodes. So, effective computational strategies for early detection of melanoma are essential. There are four principal types of skin melanoma with two sub types: Superficial spreading, nodular, lentigo, lentigo maligna, Acral lentiginous, and Subungual melanoma. Amelanotic melanoma, one particular type of melanoma, exists in all kinds of skin tones. Classifications of melanoma with its classes are focused on in this research. The ensemble classifier models, namely Adaboost, random forest, voted ensemble, voted CNN, Boosted SVM, and Boosted GMM, have been used in melanoma classification to address misclassification errors, overfitting issues, and improve accuracy. The results of the ensemble classifier achieve high classification accuracy. However, imbalanced classification is found in all six classes of melanoma. Transfer learning and ensembled transfer learning approaches are implemented to reduce the imbalanced classification issues, and performances are analyzed. Four ML/DL models, six ensembled models, four transfer learning models, and five ensembled transfer learning models are used in this investigation. Implementation of all the 19 classifiers is analyzed using standard performance metrics such as Accuracy, Precision, recall, Mathew's correlation coefficient, Jaccard Index, F1 measure, and Cohen's Kappa.

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Relation Explore Convolutional Block Attention Module for Skin Lesion Classification

Su,

Hamed,

Zhou

2024

Int J Imaging Syst Tech

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Skin cancer remains a significant global health concern, demanding accurate and efficient diagnostic solutions. Despite advances in convolutional neural networks for computer vision, automated skin lesion diagnosis remains challenging due to the small lesion region in images and limited inter‐class variation. Accurate classification depends on precise lesion localization and recognition of fine‐grained visual differences. To address these challenges, this paper proposes an enhancement to the Convolutional Block Attention Module, referred to as Relation Explore Convolutional Block Attention Module. This enhancement improves upon the existing module by utilizing multiple combinations of pooling‐based attentions, enabling the model to better learn and leverage complex interactions during training. Extensive experiments are conducted to investigate the performance of skin lesion diagnosis when integrating Relation Explore Convolutional Block Attention Module with ResNet50 at different stages. The best‐performing model achieves outstanding classification results on the publicly available HAM10000 dataset, with an Accuracy of 97.63%, Precision of 88.98%, Sensitivity of 82.86%, Specificity of 97.65%, and F1‐score of 85.46%, using fivefold cross‐validation. The high performance of this model, alongside the clear interpretability provided by its attention maps, builds trust in automated systems. This trust empowers clinicians to make well‐informed decisions, significantly enhancing the potential for improved patient outcomes.

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An Interpretable Deep Learning Model for Skin Lesion Classification

Cited by 3 publications

References 15 publications

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Skin Cancer Image Detection and Classification by CNN based Ensemble Learning

Relation Explore Convolutional Block Attention Module for Skin Lesion Classification

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