Research on SVM and KNN Classifiers for Skin Cancer Detection

,; Murugan, A.; Nair, Dr.S.Anu H; ,; Kumar, Dr.K.P. Sanal; ,

doi:10.35940/ijeat.b5117.129219

Cited by 3 publications

(2 citation statements)

References 13 publications

(15 reference statements)

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“…Keras runs on top of Tensor-Flow; so, it is not a candidate for TensorFlow. Keras uses a very simple sentence model to create a brain organization, transforms text into a ten-gram flow model, and combines this with the power of text to power the entire learning tool [32][33][34].…”

Section: Model Evaluationmentioning

confidence: 99%

Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

Viknesh,

Kumar,

Seetharaman

et al. 2023

Diagnostics

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Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.

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Section: Model Evaluationmentioning

confidence: 99%

Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

Viknesh,

Kumar,

Seetharaman

et al. 2023

Diagnostics

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“…In the study, a region of interest (ROI) and a transfer learning technique were employed to detect skin cancer, and they were able to attain dermatologist-level diagnostic accuracy [14]. Similarly, Muruguan et al [15] proposed a four-stage skin cancer detection method: preprocessing, segmentation, feature extraction, and classification. The method using SVM and K-nearest neighbor classifiers performed very efficiently in the classification process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

Aydin

2023

Diagnostics

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Among the most serious types of cancer is skin cancer. Despite the risk of death, when caught early, the rate of survival is greater than 95%. This inspires researchers to explore methods that allow for the early detection of skin cancer that could save millions of lives. The ability to detect the early signs of skin cancer has become more urgent in light of the rising number of illnesses, the high death rate, and costly healthcare treatments. Given the gravity of these issues, experts have created a number of existing approaches for detecting skin cancer. Identifying skin cancer and whether it is benign or malignant involves detecting features of the lesions such as size, form, symmetry, color, etc. The aim of this study is to determine the most successful skin cancer detection methods by comparing the outcomes and effectiveness of the various applications that categorize benign and malignant forms of skin cancer. Descriptors such as the Local Binary Pattern (LBP), the Local Directional Number Pattern (LDN), the Pyramid of Histogram of Oriented Gradients (PHOG), the Local Directional Pattern (LDiP), and Monogenic Binary Coding (MBC) are used to extract the necessary features. Support vector machines (SVM) and XGBoost are used in the classification process. In addition, this study uses colored histogram-based features to classify the various characteristics obtained from the color images. In the experimental results, the applications implemented with the proposed color histogram-based features were observed to be more successful. Under the proposed method (the colored LDN feature obtained using the YCbCr color space with the XGBoost classifier), a 90% accuracy rate was achieved on Dataset 1, which was obtained from the Kaggle website. For the HAM10000 data set, an accuracy rate of 96.50% was achieved under a similar proposed method (the colored MBC feature obtained using the HSV color space with the XGBoost classifier).

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Classification of pigmented lesion using novel k-nearest neighbor comparing with logistic regression algorithm for better accuracy

Sha,

Saravanan

2024

Fifth International Conference on Applied Sciences: Icas2023

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Research on SVM and KNN Classifiers for Skin Cancer Detection

Cited by 3 publications

References 13 publications

Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

Classification of pigmented lesion using novel k-nearest neighbor comparing with logistic regression algorithm for better accuracy

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