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This proposed work implements various classification approaches to classify the H&amp;E-stained Follicular Lymphoma tissue sample. As part of the process, k-means clustering is used to isolate cytological components like the nucleus extracellular and cytoplasmic regions from images. Then feature space vector is constructed by combining global feature extraction techniques like LBP, LDP, GLCM, and other local feature-extraction techniques. To classify FL images into their respective grades, feature space vectors obtained from feature extraction algorithms are input into a multiclass SVM. Moreover, classification accuracies were explicitly tested with different classifiers like CNN and other pre-trained deep learning networks that can directly operate on raw images without any preprocessing to classify the FL images into their respective grades. The efficacy of different classifiers is presented. This preliminary work provides a proof of concept for incorporating automated FL tissue diagnostic systems into future pathology workflows to supplement the pathologists'.

Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches