Abstract
Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.
Highlights
Malignant growth can occur in any internal organ, as well as in the blood cells, and it is not limited to one area
Detection and management of breast cancer are extremely successful techniques for diagnosing and managing the illness, and early detection may result in a higher chance of complete recovery
An evolutionary approach for categorizing and identifying breast cancer using machine learning and image processing has been established. is model may be used to aid in the classification and identification of skin problems by utilizing image preprocessing, feature extraction, feature selection, and machine learning methodologies. e geometric mean filter is used to improve the overall picture quality
Summary
Any region of the body might be affected by cancerous cell development. Normal cells become crowded out as the cancerous growth spreads throughout the body, making it difficult for the body to operate properly [1]. Malignant growth can occur in any internal organ, as well as in the blood cells, and it is not limited to one area. When it comes to the development and spread of malignant growths, there is a distinct difference between them. Malignancies other than tumors, such as leukemia (a blood illness), can occur in platelets or other cells of the body [2]. E aberrant development of cells is the beginning of breast malignant growth. E majority of breast cancers begin in the milk ducts before spreading to the areola Some have their origins in the organs responsible for producing milk. Malignant breast and lung development, such as colon polyps, can be categorized using the computer-aided design framework. Is article describes a machine learning and image processing-based evolutionary approach for detecting and classifying breast cancer. is model uses image preprocessing, image enhancement, segmentation, and machine learning algorithms to categorize and detect breast cancer
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