Brain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes
Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. In general, brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to a very short expected life if it is diagnosed at higher grade. Based on that, brain tumor grading is a very critical step after detecting the tumor in order to achieve an effective treating plan. In this paper, we used Convolutional Neural Network (CNN) which is one of the most widely used deep learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). The proposed CNN classifier is a powerful tool and its overall performance with accuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while the results for the uncropped lesions are 99% accuracy and 98.52% sensitivity and the results for segmented lesion images are 97.62% for accuracy and 97.40% sensitivity.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningBrain Tumor ClassificationDeep LearningSensitivitySimilar Papers 제목 키워드 기반
HMM Model for Brain Tumor Detection and Classification
Brain tumor is one of the significant problems that has taken the life of a lot of people in recent times. The brain tumor can be effectively treated if detected at an early stage. However, brain tumor detection requires…
Brain Tumor ClassificationBrain Tumor SegmentationClassificationmodelTriplet Contrastive Learning for Brain Tumor Classification
Brain tumor is a common and fatal form of cancer which affects both adults and children. The classification of brain tumors into different types is hence a crucial task, as it greatly influences the treatment that physic…
Brain Tumor ClassificationClassificationContrastive LearningData Augmentation+2Transfer learning for automatic brain tumor classification Using MRI Images.
One of the most leading death causes in the world is brain tumor. Solving brain tumor segmentation and classification by relying mainly on classical medical image processing is a complex and challenging task. In fact, me…
Brain Tumor ClassificationBrain Tumor SegmentationClassificationDeep Learning+5Brain Tumor Classification From MRI Images Using Machine Learning
Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The average five-year relative survival rate for malignant brain tumors is 35.6 percent. For proper diagnosis and efficient …
Brain Tumor ClassificationDetection and Classification of Brain tumors Using Deep Convolutional Neural Networks
Abnormal development of tissues in the body as a result of swelling and morbid enlargement is known as a tumor. They are mainly classified as Benign and Malignant. Tumour in the brain is fatal as it may be cancerous, so …
Data AugmentationDenoisingImage DenoisingSkull Stripping+1