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<p>Skin diseases affect a considerable part of the world population, but it is not easy to diagnose these diseases on time because of the limited availability of dermatologists and the similarity of lesions. This research investigates advanced image processing techniques by combining OpenCV—Python library and EfficientNet—B3 architecture in order to conduct accurate and reliable classification of skin diseases. The model combines lesion-focused pre-processing and imbalance data augmentation for better feature learning. The results of the proposed approach reached 92.6% accuracy, which was much higher than that of traditional architectures such as ResNet-50 (83%), VGG-16 (82%), and InceptionV3 (87%). These results mention the effectiveness of the compound scaling for dermatological image analysis. Clinically, the system can be used for early detection and dermatology screening as it provides a fast automated screening, especially in parts of the world with a shortage of specialists. This plays a role in better triaging and lessening the time of diagnostic delay, as well as providing more accessible dermatological care in remote populations. Overall, the proposed method provides a reliable and efficient solution for the scalable classification of skin diseases.</p>

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EfficientNet-based soft computing techniques for dermatological condition detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

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  • Jayaraman Venkatesh,
  • Rahul Aandha Krishnan Vijayalakshmi,
  • Deepa Krishnan,
  • Pachaivannan Partheeban

摘要

Skin diseases affect a considerable part of the world population, but it is not easy to diagnose these diseases on time because of the limited availability of dermatologists and the similarity of lesions. This research investigates advanced image processing techniques by combining OpenCV—Python library and EfficientNet—B3 architecture in order to conduct accurate and reliable classification of skin diseases. The model combines lesion-focused pre-processing and imbalance data augmentation for better feature learning. The results of the proposed approach reached 92.6% accuracy, which was much higher than that of traditional architectures such as ResNet-50 (83%), VGG-16 (82%), and InceptionV3 (87%). These results mention the effectiveness of the compound scaling for dermatological image analysis. Clinically, the system can be used for early detection and dermatology screening as it provides a fast automated screening, especially in parts of the world with a shortage of specialists. This plays a role in better triaging and lessening the time of diagnostic delay, as well as providing more accessible dermatological care in remote populations. Overall, the proposed method provides a reliable and efficient solution for the scalable classification of skin diseases.