Skin Cancer Classification Using Deep Learning

Raksha A

Skin Cancer Classification Using Deep Learning

August 2025

Vol-11, Issue-4

Paper ID: 27370

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

computer science

Keywords

Artificial Intelligence (AI) Deep Learning (DL) Convolutional Neural Network (CNN) Efficient Network (EfficientNet) Society for Imaging Informatics in Medicine (SIIM) International Skin Imaging Collaboration (ISIC) Transfer Learning (TL) Binary Classification (BC) Dropout Regularization (DR) Adaptive Moment Estimation (Adam) Binary Crossentropy Loss (BCE) Open Neural Network Exchange (ONNX) Web User Interface (UI) Real-Time Classification (RTC) Portable Document Format (PDF) Clinical Decision Support System (CDSS) Melanocytic Nevi (NV) Basal Cell Carcinoma (BCC) Squamous Cell Carcinoma (SCC) Rectified Linear Unit (ReLU).

Abstract

Skin cancer, particularly melanoma, remains a critical public health concern, where early diagnosis significantly improves treatment outcomes. This research project applies deep learning techniques—specifically EfficientNet architectures—for the classification of skin lesions using dermatoscopic images and patient metadata. The model leverages transfer learning on pre-trained EfficientNetB4, B5, and B7 architectures, fine-tuned using the SIIM-ISIC 2020 Melanoma Classification Dataset, which contains over 33,000 images along with contextual metadata such as age, gender, and lesion location. The binary classification task predicts whether a lesion is malignant or benign. To improve performance, the model incorporates both image and metadata inputs, uses dropout regularization, and is trained using the Adam optimizer with binary crossentropy loss. The model is further deployed via ONNX and integrated into a web interface that allows real-time image classification and downloadable PDF report generation. This system demonstrates the feasibility of using AI to support dermatological diagnostics in both clinical and remote environments.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Raksha A	CMR university

How to Cite

Use the following formats to cite this article in your research.

APA Style

A, Raksha (2025). Skin Cancer Classification Using Deep Learning. International Journal of Advance Research and Innovative Ideas In Education, 11(4), 3722-3729.

MLA Style

A, Raksha. "Skin Cancer Classification Using Deep Learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, 2025, pp. 3722-3729.

IEEE Style

Raksha A, "Skin Cancer Classification Using Deep Learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, pp. 3722-3729, 2025.

Vancouver Style

A Raksha. Skin Cancer Classification Using Deep Learning. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(4):3722-3729.

Harvard Style

A, Raksha (2025) 'Skin Cancer Classification Using Deep Learning', International Journal of Advance Research and Innovative Ideas In Education, 11(4), pp. 3722-3729.

Chicago Style

A, Raksha. "Skin Cancer Classification Using Deep Learning." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3722-3729.

Turabian Style

A, Raksha. "Skin Cancer Classification Using Deep Learning." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3722-3729.