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

ABSTRACT

This proposed work implements various classification approaches to classify the H&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'.

KEYWORDS:

• Deep learning
• image classification
• image segmentation
• follicular lymphoma
• centroblast
• machine learning
• cancer
• data analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Pranshu Saxena

Pranshu Saxena received his B. E. degree in Information Technology from the University of Pune, India in 2010 and the M. Tech. degree in Computer Science Engineering from LPU, India in 2013. Currently, he is working as an Assistant Professor in the Department of Information Technology at ABES Engineering College, India. His research interests include medical image processing, automated image segmentation, Image texture analysis, and intelligent system.

Anjali Goyal

Dr Anjali Goyal has received her Bachelor degree in Electronics in 1993 from Kurukshetra University and Master degree in Computer Applications in 1996 from Panjab University, Chandigarh. She has received her Ph.D degree from Punjab Technical University, Jalandhar, India in 2013. Presently she is working as Associate Professor in Department of Computer Application at Guru Nanak Institute of Management and Technology, Ludhiana, India. Her research interests include Content Base Image Retrieval, Digital Watermarking, Machine learning and Pattern recognition. She has a number of International journal and conference publications to her credit. She is reviewer of many reputed International journals.