Health Intelligent Systems Improve Value of Cancer Care and Prognosis: A Proposed Medical System and Model For Disease Management and Detection

Document Type

Article

Source of Publication

Procedia Computer Science

Publication Date

1-1-2024

Abstract

Skin cancer has emerged as a prevalent form of cancer, witnessing an upward trend in incidence over recent decades. The traditional methods for skin cancer identification are time-consuming and resource-intensive. Presently, the field of medical science leverages digital technology tools for efficient skin cancer classification. This study addresses challenges stemming from a shortage of annotated data samples for binary classification in skin cancer. Within this investigation, we introduce the single convolutional neural network (S-CNN) with multi-output functionality. The architecture of the S-CNN is intricately designed, encompassing multiple layers dedicated to extracting low to high-level features from skin images. Additionally, we integrate customized transfer learning models, specifically VGG-16 and VGG-19, into our study. Experiments were conducted utilizing a dataset comprising benign and malignant cases. The S-CNN model showcased remarkable accuracy, achieving a 96.66% success rate in effectively distinguishing between benign and malignant instances. Our automated model consistently demonstrated exceptional accuracy and performance in a comprehensive comparison with alternative methodologies.

ISSN

1877-0509

Publisher

Elsevier BV

Volume

251

First Page

311

Last Page

317

Disciplines

Computer Sciences

Keywords

Skin cancer, Convolutional neural network, Medical technology, Disease management, Prognosis

Indexed in Scopus

no

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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