Milena Abbiati, Mathieu R Nendaz, Bernard Cerutti, Monika Brodmann Mäder, Giatgen A Spinas, David Vicente Alvarez, Douglas Teodoro, Georges L Savoldelli, Nadia M Bajwa
Exploring Medical Career Choice to Better Inform Swiss Physician Workforce Planning: Protocol for a National Cohort Study
JMIR research protocols
brain tumors critical neurological ailments caused uncontrolled cell growth brain skull often leading death increasing patient longevity rate requires prompt detection however complexities brain tissue make early diagnosis challenging hence automated tools necessary aid healthcare professionals study particularly aimed improving efficacy computerized brain tumor detection clinical setting deep learning model hence novel thresholdingbased mri image segmentation approach transfer learning model based contour contourtlnet suggested facilitate clinical detection brain malignancies initial phase model utilizes contourbased analysis critical object detection precise segmentation capturing subtle variations tumor morphology model employs vgg16 architecture priorly trained imagenet collection feature extraction categorization model designed utilize ten nontrainable three trainable convolutional layers three dropout layers proposed contourtlnet model evaluated two benchmark datasets four ways among unseen case considered clinical aspect validating deep learning model unseen data crucial determine models generalization capability domain adaptation robustness realworld applicability presented models outcomes demonstrate highly accurate classification unseen data achieving perfect sensitivity negative predictive value npv 100 9860 specificity 9912 precision 9956 f1score 9946 accuracy additionally outcomes suggested model compared stateoftheart methodologies enhance effectiveness proposed solution outperforms existing solutions seen unseen data potential significantly improve brain tumor detection efficiency accuracy leading earlier diagnoses improved patient outcomes