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ORIGINAL RESEARCH article

Front. Med.
Sec. Family Medicine and Primary Care
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1436470
This article is part of the Research Topic The Impact of Primary Care on Cancer Screening Program Performance: Strategies to Increase Uptake and Effectiveness View all articles

Enhanced Skin Cancer Diagnosis through Grid Search Algorithm-Optimized Deep Learning Models for Skin Lesion Analysis

Provisionally accepted
Rudresh Pillai Rudresh Pillai 1Neha Sharma Neha Sharma 1Sheifali Gupta Sheifali Gupta 1Deepali Gupta Deepali Gupta 1Sapna Juneja Sapna Juneja 2Saurav Mallik Saurav Mallik 3*Hong Qin Hong Qin 4*
  • 1 Chitkara University, Chandigarh, India
  • 2 KIET Group of Institutions, Ghaziabad, Uttar Pradesh, India
  • 3 Harvard University, Cambridge, Massachusetts, United States
  • 4 University of Tennessee at Chattanooga, Chattanooga, Tennessee, United States

The final, formatted version of the article will be published soon.

    Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)—CNN1, CNN2, and CNN3—each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal hyperparameters for the proposed CNN models were determined using the Grid Search Optimization technique. This approach determines optimal values for architectural and fine-tuning hyperparameters, which is essential for learning. Rigorous evaluations of loss, accuracy, and confusion matrix thoroughly assessed the performance of the CNN models. Three datasets from the International Skin Imaging Collaboration (ISIC) Archive were utilized for the classification tasks. The primary objective of this study is to create a robust CNN system that can accurately diagnose skin lesions. Three separate CNN models were developed using the labeled ISIC Archive datasets. These models were designed to accurately detect skin lesions, assess the malignancy of the lesions, and classify the different types of lesions. The results indicate that the proposed CNN models possess robust capabilities in identifying and categorizing skin lesions, aiding healthcare professionals in making prompt and precise diagnostic judgments. This strategy presents an optimistic avenue for enhancing the diagnosis of skin cancer, which could potentially decrease avoidable fatalities and extend the lifespan of people diagnosed with skin cancer. This research enhances the discipline of biomedical image processing for skin lesion identification by utilizing the capabilities of DL algorithms.

    Keywords: deep learning (DL), Convolutional neural network (CNN), Grid search algorithm, binary classification, multiclass classification, Skin Cancer, Skin lesions

    Received: 22 May 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Pillai, Sharma, Gupta, Gupta, Juneja, Mallik and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Saurav Mallik, Harvard University, Cambridge, MA 02138, Massachusetts, United States
    Hong Qin, University of Tennessee at Chattanooga, Chattanooga, 37403, Tennessee, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.