Advanced skin lesion diagnosis with efficientnet-b7 feature extraction and SVM classification
Year: 2025
Type: article
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.
DOI: 10.14419/hgnb5045
Institutions: Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Gayatri Vidya Parishad College of Engineering for Women, Anil Neerukonda Institute of Technology & Sciences, GMR Institute of Technology, Koneru Lakshmaiah Education Foundation, Centurion University of Technology and Management
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409807538