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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

SKIN CANCER DETECTION AND CLASSIFICATION USING DEEP LEARNING

Revan Kale Saurav Mane Omkar Padule Onkar Pandhare Y. N. Sakhare

Department of Information Technology

Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Baramati, Pune, India
DOI:https://doi.org/10.55041/ijcope.v2i6.252

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Abstract

Prompt identification of skin disorders is a clinically significant step in curbing the progression of life-threatening conditions, most notably melanoma. Despite the availability of high-resolution dermoscopic imaging, distinguishing between disease categories remains a persistent difficulty because many lesion types share closely overlapping visual traits, and image quality is often compromised by hair, shadows, and inconsistent illumination.

We address these difficulties by developing a composite deep learning architecture that unifies convolutional and attention-based learning within one trainable system. EfficientNetV2-S forms the convolutional backbone responsible for extracting texture-level and boundary-level characteristics, while a Vision Transformer processes the resulting feature representations to capture long-range structural context across the entire lesion.

A key design choice is the use of U-Net-generated segmen-tation masks as an additional input channel. Concatenating the predicted mask with the original three-channel image produces a four-channel tensor that directs the network’s attention toward the pathological region, substantially reducing the influence of uninformative background content.

Testing on a twelve-category benchmark of 20,195 dermoscopic images yields a validation accuracy of 89.14%. The model sus-tains reliable discrimination even between visually indistinct class pairs such as melanoma and melanocytic nevus, and outperforms a single-stage CNN baseline by nearly eight percentage points. These results confirm that blending segmentation-guided input construction with a hybrid CNN-Transformer architecture is a viable and effective strategy for computer-aided skin disease diagnosis.

Index Terms—Deep Learning, Skin Disease Classification, Computer-Aided Diagnosis, EfficientNetV2, Vision Transformer (ViT), Image Segmentation, Medical Image Analysis

How to Cite this Paper

Kale, R., Mane, S., Padule, O., Pandhare, O. & Sakhare, Y. N. (2026). Skin Cancer Detection and Classification Using Deep Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.252

Kale, Revan, et al.. "Skin Cancer Detection and Classification Using Deep Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.252.

Kale, Revan,Saurav Mane,Omkar Padule,Onkar Pandhare, and Y. Sakhare. "Skin Cancer Detection and Classification Using Deep Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.252.

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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jun 19 2026
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