Enhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma...

Area of Science:

• Dermatology
• Artificial Intelligence
• Medical Imaging

Background:

• Early detection of skin cancer is crucial for patient outcomes.
• Manual diagnosis of dermoscopy images is labor-intensive and error-prone.
• Automated diagnostic tools are needed to improve efficiency and accuracy.

Purpose of the Study:

• To develop a deep learning-based approach for automatic melanoma detection.
• To enhance the performance of Convolutional Neural Networks (CNNs) using metaheuristics.
• To provide a reliable tool for early skin cancer identification.

Main Methods:

• Image preprocessing techniques were applied to dermoscopy images.
• An optimized Convolutional Neural Network (CNN) was developed for melanoma classification.
• The CNN was trained using the Improved Chameleon Swarm Algorithm (CSA) for performance optimization.
• Validation was performed on the SIIM-ISIC Melanoma dataset.

Main Results:

• The proposed deep learning method demonstrated high accuracy in diagnosing melanoma.
• Optimized CNN performance through the Improved Chameleon Swarm Algorithm (CSA) was confirmed.
• The approach showed significant potential for reliable automated skin cancer detection.

Conclusions:

• The developed deep learning model offers an effective solution for automatic melanoma detection.
• This method can assist clinicians in timely and accurate skin cancer diagnosis.
• The approach holds promise as a valuable tool for early cancer detection initiatives.