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Analyzing Skin Cancer Detection Efficiency With Convolutional Neural Networks

Analyzing Skin Cancer Detection Efficiency With Convolutional Neural Networks

N. Nagarani (Velammal College of Engineering and Technology, India), S. Seronica (Velammal College of Engineering and Technology, India), S. Rajalakshmi (Velammal College of Engineering and Technology, India), and S. J. Sherine Santhi (Velammal College of Engineering and Technology, India)
Copyright: © 2023 |Pages: 13
DOI: 10.4018/979-8-3693-1718-1.ch016
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Abstract

Skin cancer is a malignancy that develops in the skin and can cause damage, disability and even death. It occurs when skin cells grow and multiply in an uncontrolled and disordered manner, leading to a rapid growth of cells, including abnormal cells.In Indonesia, skin cancer ranks as the third most common type of cancer after cervical and bone cancer. To address the challenges in diagnosing skin cancer, the proposed study developed a system that could automatically identify skin cancer and benign growth lesions using Convolutional Neural Network(CNN) technology. The data collected from the ISIC dataset was classified into two classes: non-melanoma and carcinoma and the results achieved surpassed the performance of the skin cancer classification system. While numerous computer based individual styles using dermoscopy images have been proposed to help clinicians and dermatologists diagnose skin cancer, the proposed CNN based system offers a more efficient and accurate way of detecting and diagnosing skin cancer.
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Datasetcollection (Isic)

Melanoma

The most dangerous type of skin cancer is melanoma, a kind of “black tumor.” It's growing fast, and it's capable of spreading to any organ. Skin cells called melanocytes are responsible for melanoma. Marco Rastrelli, et al (2014) They're producing melanin, a dark pigment that gives their skin its color. The majority of melanomas are black or brown, but some are pink, red, purple, or flesh colored.

Figure 1.

Melanoma skin cancer

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Non Melanoma

One of the most rare types of cancer in the world is Non-melanoma. It's affecting a few more men than women. It’s estimated that rudimentary cell melanoma will spread to other areas of the body in fewer than half of cases. The threat is somewhat advanced in cases of squamous cell melanoma, which spread to other parts of the body in around 2to5 percent of cases.

Figure 2.

Non melanoma skin cancer

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Convolutional Neural Network

Convolutional Neural Networks (CNN or Convent) Marwan (2019) are a subset of machine power. For each operation and data type it is one of a variety of types of artificial neural networks. In particular, for image recognition and tasks that involve processing pixel data, CNNs are a type of network architecture used in deep learning algorithms.

Figure 3.

Architecture of convolutional neural network(CNN)

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Convolution Layer

Computational maturation in a convolution subspace, which is the basic building block of CNN. The original convolutional sub cast can be followed by a different convolutional sub cast. In the complication process, the core or slime inside this sub box moves over the open fields of the image and checks if there is a point in the image. During multiple copies, the kernel sweeps the entire image. Khalid M. Hosny, et al (2019) After each reproduction, the dot product is calculated between the input pixels and the mud. A dot plot or convolutional dot is the end result of a series of dots. Finally, the image is converted into numerical values ​​in this sub box, which allows the CNN to interpret the image and extract applicable patterns from it. S. Khan etal (2018)

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