Skin Cancer Classification Using Convolution Neural Networks

Abhishek, N.; Bardhan, Dibyajit; Ghosh, Anisha Ankita; Mohanty, Subhadarshini

doi:10.1007/978-981-15-4218-3_42

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…By choosing relevant features/parameters from different structures, we present some Novel Convolutional Neural Network models and structures with high accuracy. Model evaluation and its performance compared to some published network structures interactive Neural Network Architecture [12].…”

Section: Precise Novel Convolutional Neural Networkmentioning

confidence: 99%

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An Efficient Skin Cancer Prognosis Strategy Using Deep Learning Techniques

Sirisati¹,

Kumar²,

Latha³

2021

INDJCSE

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To start the right treatment, identification of an indefinite skin lesion is necessary. Only highly trained dermatologists who can treat them with an early diagnosis and diagnose melanoma skin lesions. The classification of skin for melanoma Dermoscopic images is 70 percent. Due to the limited supply of expertise, systems that sort dermal growth as the autoimmune or metastatic tumor can serve as an early screening tool. This study provides a model of the Convolutional neural network trained for skin lesion images, from previously acquired features of the Highway Convolutional neural network (CNN). It does not require advanced preprocessing. In addition, the model not require much computing power to train. The Convolutional neural network (CNN) method achieves training accuracy of 50%, and 70% of the test data have classification accuracy, low, moderate and high accuracy of the estimated damage.

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Section: Precise Novel Convolutional Neural Networkmentioning

confidence: 99%

“…Characteristic mining is an essential pace in acquiring artificial intelligence. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Various techniques for removing various kinds of carcinoma. However, there are drawbacks to these techniques, depending on characteristic mining.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Skin Cancer Prognosis Strategy Using Deep Learning Techniques

Sirisati¹,

Kumar²,

Latha³

2021

INDJCSE

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“…Each year, skin cancer cases increase and result in a higher number of deaths. Currently, approximately 3 million non-melanoma and 132,000 melanoma skin cancer cases are diagnosed globally every year [ 3 ]. According to the WHO, 9500 people receive a skin cancer diagnosis every day in the US alone, and two people die every hour [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

et al. 2021

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Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

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“…M. E. Tenekeci 520 ADYU Mühendislik Bilimleri Dergisi 18 (2022) [519][520][521][522][523][524][525][526][527][528][529] Treatment for these conditions costs, on average, USD 3.3 and USD 4.8 per year. Invasive melanoma categories have grown by 47% in the last 10 years, according to statistics [3,4]. There are around 100,000 diagnoses of melanomas detected each year in Europe.…”

Section: Introductionmentioning

confidence: 99%

“…There may be a problem with relying solely on the human eye to detect melanoma in dermoscopic images if the dermatologist's expertise varies from person to person. An untrained specialist can diagnose melanoma from dermoscopic pictures with an accuracy of 75% to 84% [3,7]. The use of Computer Aided Diagnosis (CAD) tools is required to help experts diagnose melanoma and overcome the challenges they face.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions

Zebari

Tenekeci

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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The segmentation of skin lesions is crucial to the early and accurate identification of skin cancer by computerized systems. It is difficult to automatically divide skin lesions in dermoscopic images because of challenges such as hairs, gel bubbles, ruler marks, fuzzy boundaries, and low contrast. We proposed an effective method based on K-means and a trainable machine learning system to segment regions of interest (ROI) in skin cancer images. The proposed method was implemented in several stages, including grayscale image conversion, contrast image enhancement, artifact removal with noise reduction, skin lesion segmentation from image using K-means clustering, and ROI segmentation from unwanted objects using a trainable machine learning system. The proposed model has been evaluated using the ISIC 2017 publicly available dataset. The proposed method obtained a 90.09 accuracy rate, outperforming several methods in the literature.

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Skin Cancer Classification Using Convolution Neural Networks

Cited by 13 publications

References 9 publications

An Efficient Skin Cancer Prognosis Strategy Using Deep Learning Techniques

An Efficient Skin Cancer Prognosis Strategy Using Deep Learning Techniques

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions

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