Deep Learning‐Based Skin Diseases Classification using Smartphones

Oztel, Ismail; Yolcu Oztel, Gozde; Sahin, Veysel Harun

doi:10.1002/aisy.202300211

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…Recently, several studies have explored innovative approaches in healthcare technology, focusing on AI for remote patient monitoring (RPM) and skin disease classification [115][116][117][118][119][120][121][122][123][124]. These studies established the synergy between emerging technologies like IoMT devices, and mobile applications in tackling complex skin disease challenges.…”

Section: The Trends Of Internet Of Medical Things and Remote Patient ...mentioning

confidence: 99%

“…They emphasized the integration of IoMT technologies like cloud, fog, edge, and blockchain in RPM. Oztel, Oztel and Sahin [116] and Shahin, Chen, Hosseinzadeh, Koodiani, Shahin and Nafi [120] developed a deep learning-based mobile application for skin disease classification, leveraging smartphone cameras and TensorFlow Lite to assist in preliminary diagnoses and reduce patient stress. Their user-friendly technology achieved notable accuracy in classifying skin diseases.…”

Section: The Trends Of Internet Of Medical Things and Remote Patient ...mentioning

confidence: 99%

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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: The Trends Of Internet Of Medical Things and Remote Patient ...mentioning

confidence: 99%

Section: The Trends Of Internet Of Medical Things and Remote Patient ...mentioning

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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Vision transformer and CNN-based skin lesion analysis: classification of monkeypox

Yolcu Oztel

2024

Multimed Tools Appl

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Monkeypox is an important health problem. Rapid diagnosis of monkeypox skin lesions and emergency isolation when necessary is essential. Also, some skin lesions, such as melanoma, can be fatal and must be rapidly distinguished. However, in some cases, it is difficult to distinguish the lesions visually. Methods such as dermoscopy, high-resolution ultrasound imaging, etc. can be used for better observation. But these methods are often based on qualitative analysis, subjective and time-consuming. Therefore, in this study, a quantitative and objective classification tool has been developed to assist dermatologists and scientists. The proposed system classifies seven skin lesions, including monkeypox. A popular approach Vision Transformer and some popular deep learning convolutional networks have been trained with the transfer learning approach and all results have been compared. Then, the models that show the best accuracy score have been combined to make the final prediction using bagging-ensemble learning. The proposed ensemble-based system produced 81.91% Accuracy, 65.94% Jaccard, 87.16% Precision, 74.12% Recall, and 78.16% Fscore values. In terms of different criteria metrics, the system produced competitive or even better results than the literature.

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A Deep Learning Based Smartphone Application for Early Detection of Nasopharyngeal Carcinoma Using Endoscopic Images

Yue,

Zeng,

Lin

et al. 2024

Preprint

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Nasal endoscopy is crucial for the early detection of nasopharyngeal carcinoma (NPC), but its accuracy relies significantly on the physician's expertise, posing challenges for primary healthcare providers. Here, we retrospectively analysed 39,340 nasal endoscopic white-light images from three high-incidence NPC centres, utilising eight advanced deep learning models to develop an Internet-enabled smartphone application, "Nose-Keeper", that can be used for early detection of NPC and five prevalent nasal conditions and assessment of healthy individuals. Our App demonstrated a remarkable overall accuracy of 92.27% (95% Confidence Interval (CI): 90.66%-93.61%). Notably, its sensitivity and specificity in NPC detection achieved 96.39% and 99.91%, respectively, outperforming nine experienced otolaryngologists. Explainable artificial intelligence was employed to highlight key lesion areas, improving Nose-Keeper's decision-making accuracy and safety. Nose-Keeper can assist primary healthcare providers in diagnosing NPC and related diseases efficiently, offering a valuable resource for people in high-incidence NPC regions to manage nasal cavity health effectively.

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Deep Learning‐Based Skin Diseases Classification using Smartphones

Cited by 4 publications

References 42 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

Vision transformer and CNN-based skin lesion analysis: classification of monkeypox

A Deep Learning Based Smartphone Application for Early Detection of Nasopharyngeal Carcinoma Using Endoscopic Images

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