| dc.creator | Singh, Vinayak | |
| dc.creator | Gourisaria, Mahendra Kumar | |
| dc.creator | GM, Harshvardhan | |
| dc.creator | Rautaray, Siddharth Swarup | |
| dc.creator | Pandey, Manjusha | |
| dc.creator | Sahni, Manoj | |
| dc.creator | León Castro, Ernesto | |
| dc.creator | Espinoza Audelo, Luis F. | |
| dc.date | 2022-10-18T23:25:09Z | |
| dc.date | 2022-10-18T23:25:09Z | |
| dc.date | 2022 | |
| dc.identifier | Singh, V., Gourisaria, M. K., GM, H., Rautaray, S. S., Pandey, M., Sahni, M., Leon-Castro, E., & Espinoza-Audelo, L. F. (2022). Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique. Applied Sciences, 12(6), 2900. https://doi.org/10.3390/app12062900 | |
| dc.identifier | 2076-3417 | |
| dc.identifier | http://repositoriodigital.ucsc.cl/handle/25022009/3040 | |
| dc.description | Artículo de publicación WOS - SCOPUS | |
| dc.description | A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor. | |
| dc.language | en | |
| dc.publisher | MDPI | |
| dc.subject | Convolutional neural network (CNN) | |
| dc.subject | Machine learning (ML) | |
| dc.subject | Deep learning | |
| dc.subject | Artificial neural network (ANN) | |
| dc.subject | Brain tumor | |
| dc.subject | Medical imaging | |
| dc.title | Diagnosis of intracranial tumors via the selective CNN data modeling technique | |
| dc.type | Article | |
