| Type of Material: | Thesis |
| Title: | Prediction of Malignancy in Breast Histopathology Images with Deep Learning Model Using Resource Limited Embedded Devices and Edge Computing |
| Researcher: | Johny, Anil |
| Guide: | Madhusoodanan, K N |
| Department: | Department of Instrumentation |
| Publisher: | Cochin University of Science & Technology, Cochin |
| Place: | Cochin |
| Year: | 2022 |
| Language: | English |
| Subject: | Artifical Intelligence - Health Care | Breast Cancer Detection | CNN Model | Computer Assisted Diagnosis | Computer Science Artificial Intelligence | Embedded and Edge Computing | Engineering and Technology | Histopathology Images | Medical Images | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Instrumentation, Cochin University of Science & Technology, Cochin, Cochin; 2022 |
| Fulltext: | Shodhganga |
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| 008 | __ | 241009t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_456372 |
| 040 | __ | |aCUST_682022|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aJohny, Anil|eResearcher |
| 110 | __ | |aDepartment of Instrumentation|bCochin University of Science & Technology, Cochin|dCochin|ein|0U-0253 |
| 245 | __ | |aPrediction of Malignancy in Breast Histopathology Images with Deep Learning Model Using Resource Limited Embedded Devices and Edge Computing |
| 260 | __ | |aCochin|bCochin University of Science & Technology, Cochin|c2022 |
| 300 | __ | |axvii,205|dDVD |
| 502 | __ | |bPhD|cDepartment of Instrumentation, Cochin University of Science & Technology, Cochin, Cochin|d2022 |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |d2018|oDate of Registration |
| 520 | __ | |aComputer assisted diagnosis of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. The findings by the experts based on the analysis of such data is crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with ne |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aArtifical Intelligence - Health Care |
| 653 | __ | |aBreast Cancer Detection |
| 653 | __ | |aCNN Model |
| 653 | __ | |aComputer Assisted Diagnosis |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEmbedded and Edge Computing |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aHistopathology Images |
| 653 | __ | |aMedical Images |
| 700 | __ | |aMadhusoodanan, K N|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/528336|yShodhganga |
| 905 | __ | |afromsg |
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