Austin Leeds, Dalmas Kakule, Laura Stalter, Jackson K Mbeke, Katie Fawcett
Group structure and individual relationships of sanctuary-living Grauer's gorillas (Gorilla beringei graueri)
PloS one
brain tumor diagnosis using mri scans poses significant challenges due complex nature tumor appearances variations traditional methods often require extensive manual intervention prone human error leading misdiagnosis delayed treatment current approaches primarily include manual examination radiologists conventional machine learning techniques methods rely heavily feature extraction classification algorithms may capture intricate patterns present brain mri images conventional techniques often suffer limited accuracy generalizability mainly due high variability tumor appearance subjective nature manual interpretation additionally traditional machine learning models may struggle highdimensional data inherent mri images address limitations research introduces deep learningbased model utilizing convolutional neural networks cnnsour model employs sequential cnn architecture multiple convolutional maxpooling dropout layers followed dense layers classification proposed model demonstrates significant improvement diagnostic accuracy achieving overall accuracy 98 test dataset proposed model demonstrates significant improvement diagnostic accuracy achieving overall accuracy 98 test dataset precision recall f1scores ranging 97 98 rocauc ranging 99 100 tumor category substantiate models effectiveness additionally utilization gradcam visualizations provides insights models decisionmaking process enhancing interpretability research addresses pressing need enhanced diagnostic accuracy identifying brain tumors mri imaging tackling challenges variability tumor appearance need rapid reliable diagnostic tools