Skin Diseases Classification Using Hybrid AI Based Localization Approach

Sreekala, Keshetti; Rajkumar, N.; Sugumar, R.; Sagar, K. V. Daya; Shobarani, R.; Krishnamoorthy, K. Parthiban; Saini, Ashray; Palivela, Hemant; Yeshitla, Alazar

doi:10.1155/2022/6138490

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Josphineleela, et al [56] proposed a multi-stage faster RCNNbased iSPLInception model for skin disease classification, utilizing two datasets for performance evaluation. Sreekala, et al [57] used an enhanced CNN for skin lesion classification, incorporating feature extraction and median filtering. He, et al [58] proposed a multi-task learning CNN for joint skin lesion segmentation and classification, introducing edge prediction as an auxiliary task.…”

Section: Innovative Approaches and Combination Strategiesmentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

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This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

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Section: Innovative Approaches and Combination Strategiesmentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

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“…Despite all this, dermatologists make the diagnosis with the naked eye in most non-invasive screening tests. This may cause skin diseases, which have many different types, to be overlooked (Sreekala et al, 2022). For this reason, a system that automatically detects skin disease has recently become an active field of study in order to reduce the workload of dermatologists, minimize human errors and provide more accurate diagnosis (Velasco et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Vision Transformers and Convolutional Neural Networks for Skin Disease Classification

ASLAN

2023

icontas

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Skin diseases are one of the most common diseases in humans. Due to its many various symptoms and types, computer vision studies have been frequently applied to its diagnosis and classification. Previous studies have frequently used machine learning methods and deep learning-based Convolutional Neural Networks (CNN) for skin disease diagnosis. Although deep learning-based applications have achieved great success in terms of detection accuracy, research continues to ensure the desired performance. However, Vision Transformer (ViT), recently proposed as a competitive alternative to CNNs, is gaining increasing popularity. This paper compares ResNet18 and ResNet50 networks, important CNN models, with ViT for classifying skin disease. The comparison is applied on a dataset containing a small number of samples. In the application performed on a dataset containing skin disease images, ViT provides 68.93% classification accuracy, while ResNet18 and ResNet50 classification accuracy is 61.65% and 61.17%, respectively. Other metrics calculated along with the accuracies also prove the superiority of ViT over ResNet models. However, ViT has a big disadvantage in terms of training time.

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Integrating prior knowledge to build transformer models

Jiang,

Obi,

Nakajima

2024

Int. j. inf. tecnol.

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The big Artificial General Intelligence models inspire hot topics currently. The black box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently, especially in the medical area. Therefore, transparent and reliable AI models with small data are also urgently necessary. To build a trustable AI model with small data, we proposed a prior knowledge-integrated transformer model. We first acquired prior knowledge using Shapley Additive exPlanations from various pre-trained machine learning models. Then, we used the prior knowledge to construct the transformer models and compared our proposed models with the Feature Tokenization Transformer model and other classification models. We tested our proposed model on three open datasets and one non-open public dataset in Japan to confirm the feasibility of our proposed methodology. Our results certified that knowledge-integrated transformer models perform better (1%) than general transformer models. Meanwhile, our proposed methodology identified that the self-attention of factors in our proposed transformer models is nearly the same, which needs to be explored in future work. Moreover, our research inspires future endeavors in exploring transparent small AI models.

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Skin Diseases Classification Using Hybrid AI Based Localization Approach

Cited by 5 publications

References 30 publications

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Comparison of Vision Transformers and Convolutional Neural Networks for Skin Disease Classification

Integrating prior knowledge to build transformer models

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