Work

SkinProNet: An attention-based deep learning system for skin disease classification and segmentation

Year: 2025

Type: article

Abstract: SkinProNet is an AI-powered software tool designed for the classification and segmentation of skin lesions, including potentially life-threatening conditions like melanoma. It employs a novel hybrid deep learning architecture that combines advanced preprocessing methods with state-of-the-art models: EfficientNetV2Small for feature extraction, an optimized ACRNN for accurate classification, and U 2 -Net++ for precise lesion segmentation. This integrated approach enhances early detection and diagnosis of skin diseases. The model classifies six types of skin diseases with a high accuracy of 94.04% using both benchmark datasets and real-world clinical images. The results highlight the model’s potential as a reliable computer-aided diagnostic tool in dermatology. By leveraging attention-based mechanisms and efficient neural architectures, the software supports healthcare practitioners in diagnosing skin conditions quickly, accurately, and non-invasively.

Source: Software Impacts

DOI: 10.1016/j.simpa.2025.100798

Authors: N Annalakshmi, S Umarani

Institution: SRM Institute of Science and Technology

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $600