Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images

Abstract

Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management.

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

“…Images of both large and small tumors were used to test the consistency of our method. For effective prevention and treatment of brain cancers, early diagnosis is essential [ 81 , 82 , 83 , 84 , 85 ]. Our method successfully reduces false detections while maintaining high accuracy in locating microscopic tumor areas in pictures.…”

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

“…Their study centred on creating a computationally effective show appropriate for sending in clinical settings. Altwijri et al (2023) [22] presented a novel profound learning approach for the programmed determination of Alzheimer's infection from MRI pictures. Their strategy joined progressed neural arrange structures and accomplished competitive execution in terms of symptomatic accuracy.…”

Brain Tumor Detection with Deep Learning Methods’ Classifier Optimization Using Medical Images

“…Morphological operations and connected component analysis were used to reduce the noise in the images and to identify brain tumors at a higher rate. The results obtained were compared with CNN algorithms and high success was achieved [9].…”

“…[14] YOLO 300 0.94 [9] Hybrid Model 500 0.99 [4] VGG-16 253 0.98 [15] ResNet50 2100 0.991 [5] DenseNet121 3064 0.986 [18] U-Net BraTS 2018 0.98 [17] CNN BraTS 2020 0.80 [6] Inception-V3, DenseNet 3064 0.993 [13] ResNet50 3064 0.986 [16] VGG, GoogleNet, AlexNet 3064 0.986 [13] VGG16-VGG19 233 0.948 [12] VGG-19 121 0.873 [10] CNN 3580 0.961 [20] CNN 159 0.870 [19] CNN 985 0.85 [7] CNN 497 0.842 [11] Neuro-fuzzy 20 - [8] KNN, SVM 51 0.80…”