Enhanced Detection and Analysis of Lymphoma Cancer Through Machine Learning and Deep Learning

This study focuses on the use of ml techniques to unravel the complexity of lymph node classification, which is an important part of oncology due to its different subtypes and different treatments. This study aims to develop an accurate classification model for different types of lymphoma using image datasets in the "TRAIN_DIR" and "TEST_DIR" maps. The research combines methods such as support vector machine (SVM), decision trees, logistic regression, and naive Bayes and performs rigorous and effective training methods to optimize the performance of models. Advanced preprocessing methods and data augmentation techniques are used to improve the model’s ability to detect subtle changes in biopsy images, ultimately aiming to increase diagnostic accuracy and support self-healing strategies. This approach has the potential to improve clinical decision-making and patient care in lymphoma management. Diagnose and fix problems. The study combined machine learning algorithms to analyze biopsy images to accurately classify lymphoma subtypes. The training model focuses on improving the sensitivity and specificity of subtype detection, which is important for creating an effective patient-based treatment plan. Using the computational power of support vector machines, decision trees, logistic regression, and naive Bayes, this research can help improve oncology diagnostic tools, reducing diagnostic uncertainty and supporting more treatments. The aim of this research is to advance the work of personalized medicine by creating a powerful computational model that can reliably distinguish between lymphoma subtypes. By automating and improving the accuracy of subtype classification through machine learning, the research aims to streamline diagnostic workflows and help make timely diagnostic decisions.