Abstract
Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. An intelligent system for detecting and classifying brain tumors is essential to help physicians. The novel feature of the study is the division of brain tumors into glioma, meningioma, and pituitary using a hierarchical deep learning method. The diagnosis and tumor classification are significant for the quick and productive cure, and medical image processing using a convolutional neural network (CNN) is giving excellent outcomes in this capacity. CNN uses the image fragments to train the data and classify them into tumor types. Hierarchical Deep Learning-Based Brain Tumor (HDL2BT) classification is proposed with the help of CNN for the detection and classification of brain tumors. The proposed system categorizes the tumor into four types: glioma, meningioma, pituitary, and no-tumor. The suggested model achieves 92.13% precision and a miss rate of 7.87%, being superior to earlier methods for detecting and segmentation brain tumors. The proposed system will provide clinical assistance in the area of medicine.
Highlights
Abdul Hannan Khan,1,2 Sagheer Abbas,1 Muhammad Adnan Khan,3,4 Umer Farooq,5 Wasim Ahmad Khan,1 Shahan Yamin Siddiqui,1,2 and Aiesha Ahmad6
An intelligent system for detecting and classifying brain tumors is essential to help physicians. e novel feature of the study is the division of brain tumors into glioma, meningioma, and pituitary using a hierarchical deep learning method. e diagnosis and tumor classification are significant for the quick and productive cure, and medical image processing using a convolutional neural network (CNN) is giving excellent outcomes in this capacity
Hierarchical Deep Learning-Based Brain Tumor (HDL2BT) classification is proposed with the help of CNN for the detection and classification of brain tumors. e proposed system categorizes the tumor into four types: glioma, meningioma, pituitary, and no-tumor. e suggested model achieves 92.13% precision and a miss rate of 7.87%, being superior to earlier methods for detecting and segmentation brain tumors. e proposed system will provide clinical assistance in the area of medicine
Summary
Salçin [8] proposed the mechanism to diagnose brain tumors in early stages. Magnetic resonance imaging (MRI) images have been analyzed to detect the regions containing tumors and classify these regions into different tumor categories. Reference [15] combined statistical features with neural network algorithms to develop a method for the division of mind tumors. E detection of the tumor is done by directly diagnosing the infected portion from the CT scan image It predicts the image or area of tumor considered using the Gaussian Mixture Model with Expectation-Maximization algorithm and Deep Learning Convolution Neural Network (CNN). “FISHMAN” is a deep learning project used to proceed with millions of images and foretells pancreas cancer at an early stage It helps the patients for better treatment for cure. Ito et al [19] proposed a semisupervised learning method for brain tumor segmentation using MRIs. e researcher suggested a probabilistic approach to eradicate the problem of Label Propagation in the registration-based process. Dice Similarity Score was applied to calculate the system’s accuracy and the BRATS-2015 dataset was used. e novel TP-CNN model achieved 0.81 on the complete tumor, 0.76 on the core tumor, and 0.73 on enhancing tumor
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