Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Sep 21, 2026

Uploaded by Himbo

Skin cancer is a severe, potentially lethal disease, making early detection essential for effective treatment. Conventional diagnostic workflows are costly, time-intensive, and dependent on specialized medical expertise. AI approaches have been explored, yet reproducibility and explainability remain major obstacles. To overcome these limits, the study introduces a grid-based structural and dimensional explainable deep CNN with adaptive ROI thresholding, hierarchical VGG-16 feature extraction, and AICO-based self-feature optimization for hyperparameter fine-tuning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

Materials and Methods

Proposed Grid-Based Explainable CNN

ROI Extraction and Feature Extraction

AICO Self-Feature Optimization

Experiments and Results

Datasets and Evaluation Metrics

Article Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks Kavita Behara 1, Ernest Bhero 2, * and John Terhile Agee 2 Citation: Behara, K.; Bhero, E.; Agee, J.T. Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks. Int. J. Mol. Sci. 2024, 25, 1546. [https://](https://)[ ](https://)[doi.org/10.3390/ijms25031546](doi.org/10.3390/ijms25031546) Academic Editor: Ramin Ranjbarzadeh Received: 20 December 2023 Revised: 16 January 2024 Accepted: 24 January 2024 Published: 26 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Electrical Engineering, Mangosuthu University of Technology, Durban 4031, South Africa; [beharak@mut.ac.za](beharak@mut.ac.za) 2 Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu Natal, Durban 4041, South Africa; [ageej@ukzn.ac.za](ageej@ukzn.ac.za) * [Correspondence: bhero@ukzn.ac.za](Correspondence: bhero@ukzn.ac.za) Abstract: Skin cancer is a severe and potentially lethal disease, and early detection is critical for successful treatment. Traditional procedures for diagnosing skin cancer are expensive, time-intensive, and necessitate the expertise of a medical practitioner. In recent years, many researchers have developed artificial intelligence (AI) tools, including shallow and deep machine learning-based approaches, to diagnose skin cancer. However, AI-based skin cancer diagnosis faces challenges in complexity, low reproducibility, and explainability. To address these problems, we propose a novel Grid-Based Structural and Dimensional Explainable Deep Convolutional Neural Network for accurate and interpretable skin cancer classification. This model employs adaptive thresholding for extracting the region of interest (ROI), using its dynamic capabilities to enhance the accuracy of identifying cancerous regions. The VGG-16 architecture extracts the hierarchical characteristics of skin lesion images, leveraging its recognized capabilities for deep feature extraction. Our proposed model leverages a grid structure to capture spatial relationships within lesions, while the dimensional features extract relevant information from various image channels. An Adaptive Intelligent Coney Optimization (AICO) algorithm is employed for self-feature selected optimization and fine-tuning the hyperparameters, which dynamically adapts the model architecture to optimize feature extraction and classification. The model was trained and tested using the ISIC dataset of 10,015 dermascope images and the MNIST dataset of 2357 images of malignant and benign oncological diseases. The experimental results demonstrated that the model achieved accuracy and CSI values of 0.96 and 0.97 for TP 80 using the ISIC dataset, which is 17.70% and 16.49% more than lightweight CNN, 20.83% and 19.59% more than DenseNet, 18.75% and 17.53% more than CNN, 6.25% and 6.18% more than Efficient Net-B0, 5.21% and 5.15% over ECNN, 2.08% and 2.06% over COA-CAN, and 5.21% and 5.15% more than ARO-ECNN. Additionally, the AICO self-feature selected ECNN model exhibited minimal FPR and FNR of 0.03 and 0.02, respectively. The model attained a loss of 0.09 for ISIC and 0.18 for the MNIST dataset, indicating that the model proposed in this research outperforms existing techniques. The proposed model improves accuracy, interpretability, and robustness for skin cancer classification, ultimately aiding clinicians in early diagnosis and treatment. Keywords: explainable convolutional neural network; skin cancer;

FAQs

Why is early skin cancer detection critical?

Early detection enables successful treatment and helps clinicians prevent progression to more advanced stages, reducing cancer-related fatalities.

What problems with AI-based skin cancer diagnosis does the paper target?

The paper addresses complexity, low reproducibility, and limited explainability in existing AI-based diagnosis approaches.

How does the proposed model improve interpretability and accuracy?

It uses adaptive thresholding to extract the ROI, VGG-16 for hierarchical deep feature extraction, and a grid structure with dimensional features to capture spatial and channel information, while AICO optimizes features and hyperparameters.

Sep 21, 2026

Uploaded by Himbo