Author(s): Ansh Kumar Bhatia, Pravansh Walia and Rohit Rohilla

Abstract: The most deadly kind of cancer for women globally is breast cancer. Since the cause of breast cancer is unknown, there are currently no proven methods for treating or preventing the condition. Breast cancer can be successfully detected and managed with early diagnosis, and there may be a higher chance of full recovery with early detection. Early detection of breast cancer is best achieved via mammography. This device also makes it possible to identify other diseases and could reveal details regarding the type of cancer, such as benign, malignant, or normal. This article explores an evolutionary technique for categorizing and detecting breast cancer that is based on machine learning and image processing. This model helps with the classification and detection of skin diseases by combining machine learning, feature extraction, feature selection, and image preprocessing approaches. A geometric mean filter is applied to improve the quality of the picture. AlexNet is employed in feature extraction. The relief algorithm is used to pick features. The model uses machine learning techniques such as random forest, KNN, least square support vector machine, and Naïve Bayes for disease categorization and detection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

DOI: 10.33545/27076571.2023.v4.i2a.72

Pages: 34-41 | Views: 135 | Downloads: 67

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