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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Updated: Jun 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Skin lesion segmentation using deep learning algorithm with ant colony optimization.

Nadeem Sarwar¹, Asma Irshad², Qamar H Naith³

¹Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan. Nadeem_srwr@yahoo.com.

BMC Medical Informatics and Decision Making

|September 28, 2024

Summary

This summary is machine-generated.

This study introduces a Hybrid Residual Networks (ResUNet) model optimized with Ant Colony Optimization (ACO) for enhanced skin lesion classification. The AI model significantly improves diagnostic accuracy, offering a promising tool for clinical use.

Keywords:

Ant Colony Optimization Deep learning Hybrid ResUNet Medical imaging Skin lesion segmentation

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Last Updated: Jun 11, 2025

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Area of Science:

Artificial Intelligence in Medical Imaging
Computational Pathology
Machine Learning for Dermatology

Background:

Accurate skin lesion segmentation is crucial for diagnosis and surveillance.
Deep learning models offer advancements in medical image analysis.
The Hybrid ResUNet model with Ant Colony Optimization (ACO) aims to improve skin lesion diagnosis efficiency.

Purpose of the Study:

To evaluate the Hybrid ResUNet model's effectiveness in skin lesion classification.
To assess the impact of ACO on optimizing the Hybrid ResUNet model.
To bridge the gap between computational efficiency and clinical utility in AI-driven dermatology.

Main Methods:

A deep learning approach using a Hybrid ResUNet model trained on diverse skin lesion data.
Hyperparameter optimization of the Hybrid ResUNet model using Ant Colony Optimization (ACO).
Performance evaluation using accuracy, Dice coefficient, and Jaccard index, compared against ResNet and U-Net.

Main Results:

The Hybrid ResUNet model achieved high classification accuracy (95.8%), Dice coefficient (93.1%), and Jaccard index (87.5%).
Demonstrated superior performance compared to existing state-of-the-art methods.
Showcased exceptional ability in segmenting complex skin lesions, enhancing diagnostic precision.

Conclusions:

Integrating ResUNet with ACO significantly enhances skin lesion classification accuracy.
The Hybrid ResUNet model presents a viable strategy for clinical deployment of AI tools.
Future work includes exploring multi-modal imaging, alternative optimization algorithms, and clinical applicability.