Ensemble Deep Learning Methods for Detecting Skin Cancer

Mahnoor Sardar, Muhammad Majid Niazi and Fawad Nasim
Additional contact information
Mahnoor Sardar: Department of Computer Science, Superior University, Lahore 54770, Pakistan
Muhammad Majid Niazi: Department of Computer Science, Superior University, Lahore 54770, Pakistan
Fawad Nasim: The Superior University Lahore

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 1, 673-682

Abstract: Skin cancer is a common and possibly fatal condition. Effective treatment results are greatly influenced by early identification. Deep learning (DP) algorithms have demonstrated encouraging outcomes in skin cancer detection computer-aided diagnostic systems. This article investigates the many forms of skin cancer, such as melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), and offers a system for detecting skin cancer utilizing convolutional neural network (CNN) approaches, particularly the multi-model ResNet (M-ResNet) architecture. We present a ResNet architecture that is capable of handling deep networks and has increased skin cancer detection performance. The proposed approach uses a thorough pipeline to find skin cancer. The dataset first goes through pre-processing (PP) procedures, such as picture resizing, normalization, and augmentation approaches, to improve the model's capacity for generalization. The multi-model assembles, leading to improved accuracy, sensitivity, and specificity in skin cancer LEARNING Classification SYSTEM (SC-LCS) tasks. In this study FINAL highlights, the effectiveness of deep learning (DL)techniques, specifically the multi-model ResNet architecture, AND skin cancer LEARNING classification SYSTEM (SC-LCS) for skin cancer detection. The suggested framework seems to have promising results in accurately identifying different types of skin cancer, assisting in diagnosis and therapy at an early stage. Further research and development in this field can potentially contribute to improving healthcare systems and reducing the global burden of skin cancer-related EFFECTED and DEATH RATE.

Keywords: Convolutional Neural Network (CNN); Skin Cancer Detection; Deeplearning; Resnet (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://bbejournal.com/BBE/article/view/726/704 (application/pdf)
https://bbejournal.com/BBE/article/view/726 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:1:p:673-682

DOI: 10.61506/01.00254

Access Statistics for this article

Bulletin of Business and Economics (BBE) is currently edited by Dr. Muhammad Irfan Chani

More articles in Bulletin of Business and Economics (BBE) from Research Foundation for Humanity (RFH) Contact information at EDIRC.
Bibliographic data for series maintained by Dr. Muhammad Irfan Chani ().