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Application of Deep Learning Convolutional Neural Network for Melanoma Skin Cancer Classification | IEEE Conference Publication | IEEE Xplore

Application of Deep Learning Convolutional Neural Network for Melanoma Skin Cancer Classification


Abstract:

Melanoma skin cancer is a global health concern due to its life-threatening nature and visual similarity to benign skin conditions. Accurate melanoma identification requi...Show More

Abstract:

Melanoma skin cancer is a global health concern due to its life-threatening nature and visual similarity to benign skin conditions. Accurate melanoma identification requires specialized expertise, which is often scarce and expensive. This scarcity highlights the critical need for accessible and cost-effective diagnostic solutions. Leveraging deep learning, an Artificial Intelligence (AI) approach rooted in computer vision, offers a promising avenue for melanoma detection. This study utilizes Convolutional Neural Network (CNN) algorithms, particularly DenseNet121 and VGG19 architecture, to develop robust melanoma classification models. Through rigorous evaluation, DenseNet121 emerges as the optimal architecture, achieving an impressive accuracy of 0.9385 and a minimal loss of 0.1497. Furthermore, we demonstrate practical implementation by deploying the DenseNet121 model in a web-based application using the Flask framework. Our findings underscore the efficacy of deep learning in melanoma detection and highlight the feasibility of translating such advancements into user-friendly diagnostic tools, addressing the urgent need for accessible healthcare solutions in melanoma management.
Date of Conference: 24-25 July 2024
Date Added to IEEE Xplore: 20 December 2024
ISBN Information:
Conference Location: Bukittinggi, Indonesia

I. Introduction

The skin is the largest organ in the human body, serving to protect and cover the entire surface of the human body, and is a constituent of the human body [1]. One of the diseases that can originate from direct human-to-human contact or indirect contact through environmental media is skin cancer [2]. Skin cancer is one of the most commonly diagnosed cancers worldwide [3]. Skin cancer is caused by the uncontrolled growth of skin cells, including squamous cells, basal cells, and melanocytes [1] [4]. Exposure to ultraviolet (UV) radiation is the primary cause of skin cancer, as it damages the DNA of the skin cells. This UV radiation can come from sunlight, tanning beds, or sun lamps [5]. The World Health Organization (WHO) states that one of the most common types of skin cancer is melanoma skin cancer [6].

References

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