%0 Journal Article %1 oguineenhanced %A Oguine, Ozioma Collins %D 2022 %J BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning (BIJIAM) %K 3Dconvolutionalneuralnetwork 3Dsegmentation Braintumor braintumorsegmentation fullyconvolutionalneuralnetwork %N 1 %P 79–84 %R 10.54646/bijiam.013 %T Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training %U https://journals.bohrpub.com/index.php/bijiam/article/view/58 %V 1 %X A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.

@article{oguineenhanced, abstract = {A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.}, added-at = {2022-11-28T12:14:42.000+0100}, author = {Oguine, Ozioma Collins}, biburl = {https://www.bibsonomy.org/bibtex/2d340999a78e2ec695e06b7039ebff509/bijiamjournal}, doi = {10.54646/bijiam.013}, interhash = {ad6ef4703da73ef1e695edee5431e108}, intrahash = {d340999a78e2ec695e06b7039ebff509}, journal = {BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning (BIJIAM)}, keywords = {3Dconvolutionalneuralnetwork 3Dsegmentation Braintumor braintumorsegmentation fullyconvolutionalneuralnetwork}, language = {English}, month = {November}, number = 1, pages = {79–84}, timestamp = {2023-01-09T13:41:58.000+0100}, title = {Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training}, url = {https://journals.bohrpub.com/index.php/bijiam/article/view/58}, volume = 1, year = 2022 }