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Roy, Sudipta
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- Description:
- Fetal brain segmentation and gestational age prediction have been under active research in the field of medical image processing for a long time. However, both these tasks are challenging due to factors like difficulty in acquiring a proper fetal brain image owing to the fetal movement during the scan. With the recent advancements in deep learning, many models have been proposed for performing both the tasks, individually, with good accuracy. In this paper, we present Multi-Tasking Single Encoder U-Net, MTSE U-Net, a deep learning architecture for performing three tasks on fetal brain images. The first task is the segmentation of the fetal brain into its seven components: intracranial space and extra-axial cerebrospinal fluid spaces, gray matter, white matter, ventricles, cerebellum, deep gray matter, and brainstem, and spinal cord. The second task is the prediction of the type of the fetal brain (pathological or neurotypical). The third task is the prediction of the gestational age of the fetus from its brain. All of this will be performed by a single model. The fetal brain images can be obtained by segmenting it from the fetal magnetic resonance images using any of the previous works on fetal brain segmentation, thus showing our work as an extension of the already existing segmentation works. The Jaccard similarity and Dice score for the segmentation task by this model are 77 and 82%, respectively, accuracy for the type of prediction task is 89% and the mean absolute error for the gestational age task is 0.83 weeks. The salient region identification by the model is also tested and these results show that a single model can perform multiple, but related, tasks simultaneously with good accuracy, thus eliminating the need to use separate models for each task.
- Keyword:
- Medical image processing, Fetal brain segmentation, Deep learning, Fetal gestational age prediction, and Convolutional neural networks
- Subject:
- Data Science and Artificial Intelligence
- Creator:
- Ganguly, Debayan , Chatterjee, Kingshuk , Gangopadhyay, Tuhinangshu , Sarkar, Surjadeep , Halder, Shinjini , Dasgupta, Paramik , and Roy, Sudipta
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-11-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s13721-022-00394-y
- Description:
- Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5, displacement up to 6 pixels, and scale change up to 94%.
- Keyword:
- Binary pattern, Security, Legal identity for all, and Local Descriptors
- Subject:
- Artificial Intelligence and Data Science
- Creator:
- Rehman, Amjad, Saba, Tanzila, Roy, Sudipta, Harouni, Majid, Karchegani, Negar Haghani Solati, and Bahaj, Saeed Ali
- Contributor:
- Artificial Intelligence and Data Analytics Research Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia.
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Wiley
- Location:
- United States
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-04-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1002/jemt.23989
- Description:
- Purpose For radiologists, identifying and assessing thelung nodules of cancerous form from CT scans is a difficult and laborious task. As a result, early lung growing prediction is required for the investigation technique, and hence it increases the chances of a successful treatment. To ease this problem, computer-aided diagnostic (CAD) solutions have been deployed. The main purpose of the work is to detect the nodules are malignant or not and to provide the results with better accuracy. Methods A neural network model that incorporates a feedback loop is the recurrent neural network. Evolutionary algorithms such as the Grey Wolf Optimization Algorithm and Recurrent Neural Network (RNN) Techniques are investigated utilising the Matlab Tool in this work. Statistical attributes are also produced and compared with other RNN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)combinations for study. Results The proposed method produced very high accuracy, sensitivity, specificity, and precision and compared with other state of art methods. Because of its simplicity and possible global search capabilities, evolutionary algorithms have shown tremendous promise in the area of feature selection in the latest years. Conclusion The proposed techniques have demonstrated outstanding outcomes in various disciplines, outperforming classical methods. Early detection of lung nodules will aid in determining whether the nodules will become malignant or not.
- Keyword:
- Optimization, Lung Cancer, Recurrent Neural Network, and CT images
- Subject:
- Data Science and Artificial Intelligence
- Creator:
- Roy, Sudipta, Gunjan, Vinit Kumar , Shaik, Fahimudin, and Singh, Ninni
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Cham and Switzerland
- Language:
- English
- Date Uploaded:
- 10-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-11-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s12553-022-00700-8
4. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift
- Description:
- Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.
- Keyword:
- Deep Learning, Fractures, Bone Imaging, Artificial Intelligence, Radiology, and Computer Vision
- Subject:
- Data Science and Artificial Intelligence
- Creator:
- Roy, Sudipta and Meena, Tanushree
- Contributor:
- Jio Institute CVMI-Computer Vision in Medical Imaging Project
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- MDPI
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 10-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-10-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.3390/diagnostics12102420