Advanced skin lesion diagnosis with efficientnet-b7 ‎feature extraction and SVM classification

Authors

  • V Manjula Department of Computer Science and Engineering (AIML & IOT), Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engi-neer-‎ing and Technology, ‎Hyderabad, Telangana, India
  • Pala Pooja Ratnam Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (A)، Visakhapatnam, ‎Andhra Pradesh, India
  • Dr Golagani Prasanna Priya Department of Computer Science and Engineering (AIML&DS), Anil Neerukonda Institute of Technology and Sciences (A)، Sangi-‎valasa, Visakhapatnam, ‎Andhra Pradesh, India
  • Gangu Dharmaraju Department of Computer Science and Engineering, GMR Institute of Technology (A)، Rajam, Andhra Pradesh
  • Regidi Suneetha Department of Electronics and Communication Engineering, Sanketika Vidya Parishad Engineering College, Visakhapatnam, ‎Andhra ‎Pradesh, India
  • Nagamalli Arasavalli Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, KL (Deemed to be Univer-si-‎ty)، Vaddeswaram, Guntur District, Andhra Pradesh, India
  • Mirtipati Satish Kumar Department of Computer Science and Engineering, CENTURION University of Technology and Management, Vizianagaram, ‎Andhra Pradesh

How to Cite

Manjula , V. ., Ratnam , P. P. ., Priya , D. G. P. ., Dharmaraju , G. ., Suneetha , R. . ., Arasavalli , N. ., & Kumar , M. S. . (2025). Advanced skin lesion diagnosis with efficientnet-b7 ‎feature extraction and SVM classification. International Journal of Basic and Applied Sciences, 14(1), 106-112. https://doi.org/10.14419/hgnb5045

Received date: March 25, 2025

Accepted date: April 20, 2025

Published date: April 24, 2025

DOI:

https://doi.org/10.14419/hgnb5045

Keywords:

Efficientnet-B7; Skin Lesion; International Skin Imaging Collaboration (ISIC) 2020; Support Vector Machine

Abstract

Skin cancer is the most common form of cancer globally. Timely detection is crucial, since failure to identify it in the first stage may result in ‎grave consequences. Notwithstanding its apparent visibility, significant intra-class heterogeneity and inter-class homogeneity complicate its ‎identification. Current AI methodologies for detecting skin cancer are hindered by their reliance on convolutional neural networks, resulting ‎in a lack of interpretability and sluggish processing speeds. To address the issue, the study proposes a comprehensive pipeline that integrates deep learning and machine learning methodologies to enhance detection accuracy in identifying skin lesions. The dataset under con-‎consideration is the International Skin Imaging Collaboration (ISIC) 2020. Initially, we pre-process the photos to guarantee precise training and ‎categorization. The EfficientNet-B7 deep learning model is employed for feature extraction and fed into a support vector machine ‎‎(SVM) classifier. The assessment of parameters such as accuracy, precision, recall, and F1 score yielded an accuracy of 97.52% and an F1 ‎score of 98.61%. The proposed model demonstrates superior results relative to other current models‎.

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How to Cite

Manjula , V. ., Ratnam , P. P. ., Priya , D. G. P. ., Dharmaraju , G. ., Suneetha , R. . ., Arasavalli , N. ., & Kumar , M. S. . (2025). Advanced skin lesion diagnosis with efficientnet-b7 ‎feature extraction and SVM classification. International Journal of Basic and Applied Sciences, 14(1), 106-112. https://doi.org/10.14419/hgnb5045

Received date: March 25, 2025

Accepted date: April 20, 2025

Published date: April 24, 2025