Breast cancer is cancer that forms in the breast tissue. Among all cancer deaths worldwide, many women are dying, majorly due to breast cancer. Approximately 10% of women in the world are getting affected by breast ca...
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Breast cancer is cancer that forms in the breast tissue. Among all cancer deaths worldwide, many women are dying, majorly due to breast cancer. Approximately 10% of women in the world are getting affected by breast ca...
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Breast cancer is cancer that forms in the breast tissue. Among all cancer deaths worldwide, many women are dying, majorly due to breast cancer. Approximately 10% of women in the world are getting affected by breast cancer at some point in their lives. Therefore, it has become a significant health problem that needs to be dealt with urgently. However, diagnosing breast cancer at early stages can increase the survival rates of patients as they can receive prompt treatment at the correct time. Additionally, it is essential to identify which type of tumor a patient has to avoid unnecessary treatment procedures. There are two types of cancers. One is benign, and the other is malignant. Both have different treatment methods, so identifying the type plays a significant role. However, the process is tiresome and may lead to disparities among pathologists. Machine learning procedures have been found to show high prediction accuracy. In order to decrease the mortality rate due to breast cancer, it is essential to predict at early stages. Wisconsin Breast cancer datasets are used for our study. Two classification models, i.e., XGB (Extreme Gradient Boosting) classifier algorithm and Gradient boosting Classifiers, are used to work on BC datasets. Datasets have been divided into training data set (80%) and test data set (20%). Initially, threefold validation methods are employed. A later prediction experiment was carried out using an XGB classifier and various performance evaluation metrics such as Confusion matrix, recall, precision, etc. By using the results obtained from performance metrics of algorithms, the best-suited model has been chosen. In our study, the XGB classifier showed the best prediction accuracy than the Light Gradient Boosting classifier (LGBM).
Cervical cancer remains a significant health concern worldwide, especially in regions with limited access to healthcare resources. Early detection and prevention are crucial for reducing the burden of this disease. In...
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ISBN:
(数字)9798350354218
ISBN:
(纸本)9798350354225
Cervical cancer remains a significant health concern worldwide, especially in regions with limited access to healthcare resources. Early detection and prevention are crucial for reducing the burden of this disease. In recent years, deep learning has emerged as a promising tool for analyzing medical data and improving diagnostic accuracy. This article explores the application of deep learning techniques in predicting and preventing cervical cancer. Pap smear screening is used to screen for cervical cancer in order to diagnose and categorize the disease. To identify and categorize cervical tissue abnormalities, Pap smear pictures of the cervical region are used. We presented a deep learning-based approach in this paper that can categorize pap smear images into different classifications. A computer-aided diagnosis method that classifies abnormalities in cervical imaging cells is designed using Pap smear images. Seven image classes are discriminated using automated features that were derived using ResNet101. The ability of the proposed approach to differentiate between the usual case levels with 95.02sensitivity and 95.93% accuracy. Furthermore, it has a 95.93 accuracy rate in differentiating between normal and a typical instance. After that, the high degree of anomaly is examined and accurately classified.
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