Qualification: 
Ph.D, M.Tech, B-Tech
gopakumarg@am.amrita.edu

Dr. Gopakumar G. currently serves as Assistant Professor (Sl. Gr.) at the department of Computer Science and Engineering, Amrita School of Engineering, Amritapuri campus. He is a post-graduate (M.Tech.) in Digital Image Computing from the Department of Computer Science, University of Kerala (Karyavattom Campus) and a graduate in Computer Science and Engineering from College of Engineering Karunagappally. Dr. Gopakumar pursued his Ph. D. from Indian Institute of Space Science and Technology in Computer Vision (Medical Image Analysis) under the guidance of Dr. Gorthi R. K. Sai Subrahmanyam. His research thesis is entitled 'Automatic Feature Extraction and Classification of Cell images for Cytopathology'. He has 5 years of academic experience.

His research interests include Computer Vision, Image Analysis and Machine Learning. 

Education

DEGREE/PROGRAM INSTITUTION
PhD Indian Institute of Space Science & Technology
M.Tech. Digital Image Computing University of Kerala(Karyavattom Campus)
B.Tech. CSE CE Karunagappally

Publications

Publication Type: Book Chapter

Year of Publication Title

2019

Gopakumar G and Subrahmanyam, G. R. K. Sai, “Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines”, in Handbook of Deep Learning Applications, V. Emilia Balas, Roy, S. Sekhar, Sharma, D., and Samui, P., Eds. Cham: Springer International Publishing, 2019, pp. 219–257.[Abstract]


This chapter discusses a few applications of deep learning networks in cytopathology. Specifically, the detection of malaria from slide images of blood smear and classification of leukaemia cell-lines are addressed. The chapter starts with relevant theory for traditional (deep) multi-layer neural networks with back-propagation, followed by motivation, theory and training in Convolutional Neural Networks (CNN), the trending deep-learning based classifier. The detection of malaria from blood smear slide images using CNN is addressed followed by a discussion on the transfer learning capability of CNN by taking the classification of leukaemia cell-lines: K562, MOLT & HL60 as an example. The transfer learning capability of CNN is of particular interest especially when there are only very limited number of training samples to come up with a stand alone deep CNN classifier.

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Publication Type: Conference Proceedings

Year of Publication Title

2017

K. S. Kalmady, Kamath, A. S., Gopakumar G, Subrahmanyam, G. R. K. S., and Gorthi, S. S., “Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells”, 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, Bangalore, India, pp. 1-6, 2017.[Abstract]


One of the most crucial parts in the diagnosis of a wide variety of ailments is cytopathological testing. This process is often laborious, time consuming and requires skill. These constraints have led to interests in automating the process. Several deep learning based methods have been proposed in this domain to enable machines to gain human expertise. In this paper, we investigate the effectiveness of transfer learning using fine-tuned features from modified deep neural architectures and certain ensemble learning methods for classifying the leukemia cell lines HL60, MOLT, and K562. Microfluidics-based imaging flow cytometry (mIFC) is used for obtaining the images instead of image cytometry. This is because mIFC guarantees significantly higher throughput and is easy to set up with minimal expenses. We find that the use of fine-tuned features from a modified deep neural network for transfer learning provides a substantial improvement in performance compared to earlier works. We also identify that without any fine tuning, feature selection using ensemble methods on the deep features also provide comparable performance on the considered Leukemia cell classification problem. These results show that automated methods can in fact be a valuable guide in cytopathological testing especially in resource limited settings.

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2016

Gopakumar G, Swetha, M., Siva, G. Sai, and Subrahmanyam, G. R. K. S., “Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide Images”, Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing. ACM, New York, NY, USA, 2016.[Abstract]


Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported world-wide. Thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. This paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.

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2014

Gopakumar G, Subrahmanyam, G. R. K. Sai, and Siva, G. Sai, “Morphology based Classification of Leukemia Cell lines: K562 and MOLT in a Microfluidics based Imaging Flow Cytometer”, ICVGIP ’14. ACM, Bangalore, 2014.[Abstract]


This paper proposes a framework for classification of label-free, unstained, leukemia cell lines MOLT and K562 in microfluidics based Imaging Flow Cytometry (IFC). These two cell lines differ in their internal cell complexity in an IFC image. Each cell is localized by finding a closed cell membrane binding internal organelles. An existing non-iterative graph based contour detection algorithm is extended and is effectively used to segment out the cells. Features reflecting the size, circularity and internal cell complexity are extracted and used for classification using linear Support Vector Machine.

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Publication Type: Journal Article

Year of Publication Title

2017

Gopakumar G, Swetha, M., Siva, G. Sai, and Subrahmanyam, G. R. K. Sai, “Convolutional Neural Network-based malaria Diagnosis from Focus Stack of Blood Smear Images Acquired Using Custom-built Slide Scanner”, Journal of Biophotonics, p. e201700003–n/a, 2017.[Abstract]


The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2-level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for point-of-care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis

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2017

Gopakumar G, K, H. Babu, D, M., SS, G., and GR, S. Subrahmany, “Cytopathological image analysis using deep-learning networks in microfluidic microscopy”, Journal of the Optical Society of America A, vol. 34, no. 1, pp. 111-121, 2017.[Abstract]


Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

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2017

V. Kalyan Jagannadh, Gopakumar G, Subrahmanyam, G. R. K. Sai, and Gorthi, S. Siva, “Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening”, Medical {&} Biological Engineering {&} Computing, vol. 55, pp. 711–718, 2017.[Abstract]


Each year, about 7–8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate.

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2015

Gopakumar G, VK, J., SS, G., and GR, S., “Framework for morphometric classification of cells in imaging flow cytometry”, 2015.[Abstract]


Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis.

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