Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification(Springer, 2023) Kadaskar, M.; Patil, N.Deep learning has recently advanced in its applicability to computer vision challenges, and medical imaging has become the most used technique in histopathology image analysis. Nuclei instance segmentation, detection, and classification are one such task. Reliable analysis of these image slides is critical in cancer identification, treatment, and care. Researchers have recently been interested in this issue. This study reviews the categorization and investigation of strategies utilized in recent works to improve the effectiveness of automated nuclei segmentation, detection, and classification in histopathology images. It critically examines state-of-the-art deep learning techniques, analyzes the trends, identifies the challenges, and highlights and helps with the future directions for research. The taxonomy includes deep learning techniques, enhancement, and optimization methods. The survey findings will help to overcome the challenges of nuclei segmentation, detection, and classification while improving the performance of models and, thus, aid future research plans. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item A fast and novel approach based on grouping and weighted mRMR for feature selection and classification of protein sequence data(Inderscience Publishers, 2020) Kaur, K.; Patil, N.The analysis of protein sequences under bioinformatics has gained wide importance in research area. Newly added protein sequences can be analysed using existing proteins and converting them into feature vector form. However, it emerges as a challenging task to deal with huge number of features obtained using sequence encoding techniques. Since all the features obtained are not actually required, a three-stage feature selection approach has been proposed. In the first stage, features are ranked and most irrelevant features are removed; in the second stage, conflicting features are grouped together; and in third stage, a fast approach based on weighted Minimum Redundancy Maximum Relevance (wMRMR) has been proposed and applied on grouped features. Different classification methods are used to analyse the performance of the proposed approach. It is observed that the proposed approach has increased classification accuracy results and reduced time consumption in comparison to the state-of-the-art methods. © 2020 Inderscience Enterprises Ltd.Item Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images(Springer, 2023) Agrawal, S.; Venkatesh, V.; Nara, M.; Patil, N.COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50’s abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model’s explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model’s ability to generalize. The proposed model is intended to be deployed for free use. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item ANet: Nuclei Instance Segmentation and Classification with Attention-Based Network(Springer, 2024) Kadaskar, M.; Patil, N.The segmentation and classification of nuclei in haematoxylin and eosin-stained images is critical for diagnosing cancer and other disorders. Developing automated methods is necessary for the quantitative analysis of whole-slide images and further downstream analysis. However, many challenges are to be solved, such as varying morphology and observer differences. To address these concerns, we present ANet, an encoder–decoder structure based on attention mechanisms for nuclear segmentation and classification that makes use of information in high-dimensional features improved by attention. These blocks generate meaningful feature activation and eliminate irrelevant information to produce finer maps. It segments the touching, clustered, and overlapping nuclei and classifies them using upsampling branches. Our method includes components such as PreAct-ResNet50, residual attention, convolutional block attention module, and dense attention unit. We demonstrate how our approach achieves cutting-edge performance on several multi-tissue histopathology datasets such as Kumar, CoNSeP, and CPM17. We also demonstrate our model’s generalization capabilities on other combinations of datasets, including CPM15 and TNBC. ANet demonstrates a notable improvement of 1.14%, 2.70%, 1.41%, and 1.29% in Dice, AJI, SQ, and PQ scores, respectively, for the CPM17 dataset. In addition, it achieves a 1.18% improvement in AJI score for the Kumar dataset. Despite the inherent challenges in nuclei classification within the CoNSeP dataset, ANet yields outstanding results, showcasing a substantial improvement of 9.74%, 3.97%, and 0.80% in F1 scores for the inflammatory, spindle, and miscellaneous classes. Furthermore, ANet exhibits strong generalization across the CPM dataset, TNBC, and Combined CoNSeP, with improvements observed in the majority of metrics. The given improvement is justifiable, as are the interpretable visual results. The proposed method is of great potential for analyzing histopathology images, demonstrated by an increment of performance in multiple metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.