Cancer Detection Using Machine Learning
Ms. Poonam Chakravarty · International Journal for Research in Applied Science and Engineering Technology · 2024
Cancer has been classified into various subtypes. So, it is essential to detect cancer symptoms early on. Artificial Intelligence(AI) and Machine Learning(ML) have been used to classify cancer categories. There are certain datasets and models capable of estimating key features. There are many methods used for the development of cancer detection techniques, some of the methods are artificial neural networks(ANNs), and decision trees(DTs). While considering these methods and understanding them, a valid procedure must be considered for their implementation. In this study, various ML and Deep Learning(DL) approaches are explained which can be used in cancer detection. Although there has been progress in the diagnosis and treating cancer victims with personalization, it is difficult to provide cancer victims with data-driven care. To improve patient outcomes and medical efficiency, the application of Artificial Intelligence(AI) has become an effective means. Machine Learning provides an opportunity for systems to learn by gaining knowledge from learning models, and this approach is very successful at forecasting different forms of cancer, among them related to liver, lung, and other cancers. Professionals are not as precise in forecasting illness as machine learning and artificial intelligence are. The recent advancement in deep learning has transformed medical imaging, providing tools for analyzing data automatically. Convolutional neural networks, also known as CNNs, have been employed to identify several tumor categories such as MRI and CT scans. In this study, we shall learn about a structural framework that includes data preprocessing, model architecture design, and performance analysis. Deep learning models can achieve higher accuracy and sensitivity for the identification of symptoms related to cancer with an efficient approach compared to traditional practices of treatment.