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This paper shows a new way to find skin lesions using a better version of the Xception convolutional neural network (CNN). We preprocessed the HAM10000 [27] dataset, a sizable collection of 10,015 dermatoscopic images categorized into seven diagnostic groups, in order to improve model performance. Our optimized Xception model achieved 99.52% training precision and 95.21% validation precision in discriminating skin lesions. These findings show how deep learning models, particularly those with fine-tuned architectures such as Xception, can increase the accuracy and dependability of dermatological diagnoses.

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Advanced Skin Lesion Detection Using Fine-Tuned Xception

  • 会议论文

  • 发表日期：

    2026年

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  • Subarna Pal,
  • Pratik Biswas,
  • Partha Sarathi Biswas,
  • Pritam Dan,
  • Amrita Namtirtha,
  • Ira Nath,
  • Uddalak Mitra

摘要

This paper shows a new way to find skin lesions using a better version of the Xception convolutional neural network (CNN). We preprocessed the HAM10000 [27] dataset, a sizable collection of 10,015 dermatoscopic images categorized into seven diagnostic groups, in order to improve model performance. Our optimized Xception model achieved 99.52% training precision and 95.21% validation precision in discriminating skin lesions. These findings show how deep learning models, particularly those with fine-tuned architectures such as Xception, can increase the accuracy and dependability of dermatological diagnoses.