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- 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
-
- Description:
- The global healthcare sector continues to grow rapidly and is reflected as one of the fastestgrowing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
- Keyword:
- Healthcare, Precision Medicine, Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, XAI, and Supervised Learning
- Subject:
- Artificial Intelligence and Data Science
- Creator:
- Roy, Sudipta , Lim, Se-Jung , and Meena, Tanushree
- Contributor:
- Jio Institute, CVMI-Computer Vision in Medical Imaging Project
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- MDPI
- Location:
- Switzerland and India
- 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/diagnostics12102549
-
13. 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
-
- Description:
- This article attempts to comprehend the current issues and hurdles that Indian colleges affiliated with Tamil Nadu State Universities encounter when trying to subscribe to a software that detects plagiarism. The study’s goals are to determine whether colleges employ anti-plagiarism software, whether they ensure that their student given assignments are free of copyright infringement, whether tutors teach about academic misconduct, and what people seem to think of anti-plagiarism software. We surveyed for this study and distributed the questionnaires among college administrators, principals, and librarians. The study respondents are 15.9 per cent principals, 64.2 per cent library professionals, and 19.9 per cent college administrators. The survey study report shows that 70.9 per cent of the majority of the colleges did not subscribe. 41.7 per cent gave the reason it is too expensive, and 30.5 per cent of respondents thought that for their college, it is unnecessary to subscribe. However, nobody has confirmed whether or not all colleges possess access to plagiarism detection software. Thus, according to this investigation, further Indian states must be involved in this research to understand the specific context fully. This report advises the UGC to enforce the requirement that colleges have plagiarism detection software; they either provide colleges additional money to subscribe to such software, or the university must grant free access to the affiliated colleges.
- Keyword:
- Anti-plagiarism software, Academic misconduct, Obstacles, Plagiarism software subscription, and Academic integrity
- Subject:
- Library and Information Science
- Creator:
- N., Sakthivel and A., Subaveerapandiyan
- Contributor:
- Jio Institute Digital Library
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- DESIDOC Journal of Library and Information Technology
- Location:
- India
- Language:
- English
- Date Uploaded:
- 10-02-2023
- Date Modified:
- 21-03-2023
- Date Created:
- 01-10-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.14429/djlit.42.5.18273
-
- Description:
- Skin Cancer is one of the most widespread forms of cancer in the world which can be detected using dermatoscopic images. In this paper, a texture based feature extraction algorithm is presented for the classification of dermatoscopic images. A median based Local Ternary Pattern is extracted followed by the computation of local quantized ternary patterns. The feature set extracted is then classified using a modified convolutional neural network. The images used for the detection of multiple types of skin cancer are obtained from two publicly available datasets, HAM10000 and ISICUDA11. For the proposed technique, the average recall value, average precision and average accuracy is found to be 75.20%, 95.44% and 96% respectively. An average increase in accuracy for the proposed algorithm is up-to 50.6%, 24.1% and 4.7% over LTP, DLTerQEP and a DE ANN based algorithm respectively.
- Keyword:
- Medical imaging, Image classification, Image retrieval, and Texture detection
- Subject:
- Medical Imaging, Data Science, Radiodiagnosis, Computer Science, Artificial Intelligence, and Radiology
- Creator:
- Sudipta Roy , Varun Srivastava, and Deepika Kumar
- Contributor:
- Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai, India
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Elsevier
- Location:
- United States
- Language:
- English
- Date Uploaded:
- 07-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-09-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1016/j.compeleceng.2022.108259