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A brain tumor develops as a result of uncontrolled and rapid cell proliferation. If not treated in its early stages, it might result in death. Despite several significant efforts and positive outcomes, accurate segmentation and classification remain challenging jobs. The variations in tumor size, shape, and location provide a substantial challenge for brain tumor diagnosis. Therefore, identifying brain tumors manually is challenging, time-consuming, and prone to mistakes. Consequently, there is now a need for high-accuracy automated computer-assisted diagnostics. This paper proposes a novel brain tumor detection method based on a machine learning classifier. Initially, the brain tumor images are collected from the “Magnetic Resonance Imaging (MRI)” database. In the preprocessing stage, anisotropic filtering and “Adaptive Histogram Equalization (AHE)” are performed to remove the noise and enhance the image contrast respectively. Then the images are segmented using “Enhanced Fruitfly Optimization-based Otsu segmentation (EFO-OTSU)”. The feature extraction is done using “Principal Component Analysis (PCA)” and “Discrete Wavelet Transform (DWT)”. We propose Boosted “Multi-Gradient Support Vector Machine (BMG-SVM)”to use the retrieved characteristics to divide the picture into the tumor and non-tumor sections. Further to enhance the classification performance, we employ the “Black Monkey Optimization (BMO)” algorithm. A few currently used approaches are contrasted with the simulation results of the suggested technique. The final findings show that the suggested technique outperforms the other methods in terms of effectiveness.

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%). [ABSTRACT FROM AUTHOR]

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the most often diagnosed malignant tumors in persons of all ages. Recognizing its grade is challenging for radiologists in health monitoring and automated determination; however, IoT can help. It is critical to detect and classify contaminated tumor locations using Magnetic Resonance Imaging (MRI) images. Numerous tumors exist, including glioma tumor, meningioma tumor, pituitary tumor, and no tumor (benign). Detecting the type of tumor and preventing it is one of the most challenging aspects of brain tumor categorization. Numerous deep learning-based approaches for categorizing brain tumors have been published in the literature. A CNN (Convolutional Neural Network), the most advanced method in deep learning, was used to detect a tumor using brain MRI images. However, there are still issues with the training procedure, which is lengthy. The main goal of this project is to develop an IoT computational system based on deep learning for detecting brain tumors in MRI images. This paper suggests combining A CNN(Convolutional Neural Network) with an STM(Long Short Term Memory), LSTMs can supplement the ability of CNN to extract features. When used for image classification, the layered LSTM-CNN design outperforms standard CNN classification. Experiments are undertaken to forecast the proposed model's performance using the Kaggle data set, which contains 3264 MRI scans. The dataset is separated into two sections: 2870 photos of training sets and 394 images of testing sets. The experimental findings demonstrate that the proposed model outperforms earlier CNN and RNN models in terms of accuracy. [ABSTRACT FROM AUTHOR]