| Type of Material: | Thesis |
| Title: | Melanoma detection from the dermoscopic images using binary spiking neural networks |
| Researcher: | Nallanti Venkateswararao |
| Guide: | Venkateswara Rao, P |
| Department: | Department of Computer Science and Engineering |
| Publisher: | Adikavi Nannaya University |
| Place: | Rajahmundry |
| Year: | 2024 |
| Language: | English |
| Subject: | Computer Science | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Science and Engineering, Adikavi Nannaya University, Rajahmundry; 2024 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-08-25 15:35:43 | |
| 008 | __ | 260825t2024||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_462302 |
| 040 | __ | |dIN-AhILN|aANUR_533105 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aNallanti Venkateswararao |
| 110 | __ | |aDepartment of Computer Science and Engineering|0U-0005|dRajahmundry|bAdikavi Nannaya University|ein |
| 245 | __ | |aMelanoma detection from the dermoscopic images using binary spiking neural networks |
| 260 | __ | |aRajahmundry|c2024|bAdikavi Nannaya University |
| 300 | __ | |dDVD |
| 502 | __ | |d2024|bPhD|cDepartment of Computer Science and Engineering, Adikavi Nannaya University, Rajahmundry |
| 518 | __ | |d2024|oDate of Award |
| 518 | __ | |oDate of Registration|d2020 |
| 518 | __ | |oDate of Completion|d2024 |
| 520 | __ | |aMelanocytes are the cells that produce melanin, the pigment that gives skin its color. Melanoma, in particular, is a serious health problem that results from these cells. The early detection of skin lesions is essential for efficient treatment, since skin cancer cases are increasing. Nonetheless, misclassification can occur due to similarities among certain skin lesions, which is a serious issue. The fact that benign skin lesions are more common than malignant ones should be noted because this can cause algorithms to be overly cautious and produce inaccurate results. In order to find an early diagnosis of malignant tumors, researchers are creating computer-assisted diagnostic tools. Various approaches to automate the assessment of skin lesions have been investigated in earlier research, spanning from traditional classification methods to cutting-edge Deep Learning (DL) models. Traditional classification techniques usually use features that have been manually created as input. The possibility of survival is |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 700 | __ | |aVenkateswara Rao, P|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/632938|yShodhganga |
| 905 | __ | |afromsg |
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