Browsing by Author "Bygari, R."

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Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques
(Institute of Electrical and Electronics Engineers Inc., 2023) Niyas, S.; Bygari, R.; Naik, R.; Viswanath, B.; Ugwekar, D.; Mathew, T.; Kavya, J.; Kini, J.R.; Rajan, J.
Objective: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. It involves the assessment of the four molecular biomarkers namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67 using appropriate antibody reagents. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images. Methods and procedures: A modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. This architecture uses long skip connections to pass encoder feature space from different semantic levels to the decoder layers, allowing concurrent learning with multi-scale features. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. The segmentation stage is followed by a post-processing stage to quantify the extent of immunopositive elements to predict the final status for each biomarker. Results: The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. Furthermore, the biomarker prediction results are also evaluated. The results obtained by our method are highly in concordance with manual assessment by pathologists. Clinical impact: Accurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists' workload and associated costs, and facilitate targeted treatment to obtain better outcomes. © 2013 IEEE.
Blindness (Diabetic Retinopathy) Severity Scale Detection
(Institute of Electrical and Electronics Engineers Inc., 2021) Bygari, R.; Naik, R.; Uday Kumar, P.
Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. Manual diagnosis is time consuming and error-prone. In this paper, we propose a novel deep learning based method for automatic screening of retinal fundus images to detect and classify DR based on the severity. The method uses a dual-path configuration of deep neural networks to achieve the objective. In the first step, a modified UNet++ based retinal vessel segmentation is used to create a fundus image that emphasises elements like haemorrhages, cotton wool spots, and exudates that are vital to identify the DR stages. Subsequently, two convolutional neural networks (CNN) classifiers take the original image and the newly created fundus image respectively as inputs and identify the severity of DR on a scale of 0 to 4. These two scores are then passed through a shallow neural network classifier (ANN) to predict the final DR stage. The public datasets STARE, DRIVE, CHASE DB1, and APTOS are used for training and evaluation. Our method achieves an accuracy of 94.80% and Quadratic Weighted Kappa (QWK) score of 0.9254, and outperform many state-of-the-art methods. Â© 2021 IEEE.
Prostate Cancer Grading Using Multistage Deep Neural Networks
(Springer Science and Business Media Deutschland GmbH, 2023) Bygari, R.; Rithesh, K.; Ambesange, S.; Koolagudi, S.G.
Prostate cancer is the second most commonly occurring cancer in men with a high incidence to mortality ratio. Accurate prostate cancer grading is the foremost step in determining the precise treatment process for the patient in preventing mortality of the patient. Currently, the grading is carried out by pathologists, which has limitation of availability super specialist doctors across world to grade it at affordable price, and non-super specialist doctor grading is error prone. This paper evades the need for an expert pathologist by proposing a novel deep learning method for automatic screening of prostate images to detect and assign a grade severity of cancer based on the images. The explainability of classification model imbibed using gradient-weighted class activation mapping (GradCAM) visualization, which generate heatmap of image, which influenced the decision of the model. The proposed method has three stages with ensemble deep neural networks to grade the prostate cancer. Firstly, a UNet is used for the segmentation of the histopathological image. Subsequently, the segmented image is overlaid on the original image, which helps underscore the most critical regions determining the grade of cancer. Finally, the overlaid image is used by an ensemble model consisting of Xception, Resnet-50, EfficientNet-b7 to predict the final grade of the histopathological image. The dataset containing 10,000 histopathological images obtained from Karolinska and Radboud that are made publicly available through the Prostate Cancer Grade Assessment Challenge hosted in Kaggle is used for training and evaluation. This method achieves a classification accuracy of 92.38% and outperforms many state-of-the-art methods. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.