Brain Tumor Classification and Stage Detection Using Deep Learning

Brain tumors are abnormal growth of cells which are very serious issue, and the early diagnosis is the main factor for the enhancement of the patients' situation. The traditional methods of brain tumor detection and classification are usually time-consuming, and often the human error is involved. Also, they cannot at all accurately classify the different types of tumors. The deep learning models mainly CNNs have been the main factors for the significant transformation of the medical image analysis. CNNs are applied to automatically extract the complex features from MRI images which make it possible to get the advantages of the exact identification of the tumor boundaries and the slight variations that can be ignored by human. This work investigates the use of transfer learning technique to improve the accuracy of CNN models for brain tumor detection and classification. We tested VGG16 and ResNet50 models on 4000 MRI images with tumor and no-tumor. Both scratch and pre-trained models were used. Results show that pre-trained VGG16 and ResNet50 achieved 99.44% and 99.55% accuracy while scratch models achieved 59.64% and 96.02% accuracy respectively. This result proves the power of transfer learning technique in deep learning models especially CNNs for brain tumor detection and classification.

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.

In this paper, we present a study on brain tumor detection using the VGG-16 model, a convolutional neural network known to be effective for computer vision tasks. This study proposes an enhanced VGG-16 model by modifying its architecture with batch normalization and dropout layers to improve generalization and prevent overfitting. Unlike previous studies that directly applied VGG-16, this research introduces architectural changes to adapt the model for more effective multi-class classification of brain tumors. The objective of this study is to accurately determine the presence or absence of a brain tumor by classifying magnetic resonance imaging (MRI) images. The dataset used consists of MRI images of brain tumors classified into two classes: No (no tumor) and YES (tumor). The methodology includes environment setup, data import and preprocessing, VGG16 model construction, and performance evaluation using metrics such as accuracy, precision, and recall. The results show an accuracy of about 98.47% on the validation set and 98.32% on the test set, indicating that the VGG-16 model has the potential to assist medical professionals in diagnosing brain tumors. This study contributes to the field of medical image analysis and provides ideas for applying deep learning to brain tumor diagnosis.

In recent years, Magnetic Resonance Imaging (MRI) scanning has been the most rapidly developing field. Concerning the tumor’s size and specifics, diagnosing and classifying brain tumor’s is challenging and time-consuming for radiologists. The growth of abnormal cells in the brain is referred to as a brain tumor. A brain tumor is diagnosed in about 11,700 patients every year. It is estimated that 34% of males and 36% of females will survive five years after being diagnosed with malignant brain or different tumors. This study focuses on meningioma, pituitary, glioma and no tumors’, among the many brain tumors. Deep learning algorithms and machine learning methods were used to create an autonomous classification and segmentation system for brain tumors, significantly improving early detection. Using the Visual Geometry Group (16), parameters were set for training the model that detects brain tumors based on analysis of proposed literature solutions. Simple Convolution Neural Network (CNN) models such as VGG-16 and Efficient NetB7 perform well because they are among the highest-performing models. As a result of the study, quick, efficient, and precise decisions can be made using MRI to detect brain tumors. For this 7022 brain magnetic resonance images were used to train and test this model. According to experimental findings, the suggested differential deep CNN model could accurately categories MRI pictures of brain tumors, including aberrant and standard images, with a 98.19% accuracy rate.

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of highquality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co- occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GASVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

The purpose of the research is to find out how deep learning and the convolutional neural network will contribute to diagnosis, early detection and segmentation of brain tumors such as glioma, benign, malignant, etc. The aim is to achieve a higher degree of segmentation quality to resolve issues related to lack of the classification accuracy and poor performance in the segmentation and detection of tumors. The presented solution is an Advanced Cascaded Anisotropic Convolutional Neural Network (CA-CNN) architecture with an optimized feature selection method. The DFP (Data collection, feature extraction & selection and prediction) taxonomy is presented that involves data acquiring, data pre-processing, feature extraction, selection and prediction methods for effective tumor segmentation and detection. The presented system will enhance the prediction accuracy and involves the genetic algorithm for effective selection of features which prevents data redundancy and reduce the delay in the detection of tumors. The utilization of genetic algorithm minimizes the redundancy within input voxels and facilitates in the optimal selection of features which improves the classification accuracy of the solution. The research conducted is to improve the brain tumor segmentation and detection process in terms of accuracy, specificity and sensitivity using multi-scale prediction and cross-validation.

Dual-stage deep learning framework for brain tumor classification and localization using multimodal MRI scans

Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.

Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results. The model presented in this study, which offers improvements for feature extraction and classification, uses deep learning and machine learning for the assessment of brain tumors. Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception. The classification of brain tumors is explored through deep- and machine-learning algorithms including softmax, Random Forest, Support Vector Machine, K-Nearest Neighbors, and the ensemble technique. The results of these approaches are compared with existing methods. The Inception-v3 model has a test accuracy of 94.34% and achieves the highest performance compared with other recently reported methods. This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis. Furthermore, this type of approach can be used as an effective decision-support tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging (MRI) analysis. It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.

Brain tumor is generally structured by the subtle summation of anomalous cells because it categorized as a mass of tissue, and it is very crucial to categorize brain tumors from Magnetic Resonance Imaging (MRI) for diagnosis purpose. The investigation of brain tumors by humans is a routine process for detecting and classifying MRI brain tumors. Due to the low contrast of the images, poor boundaries and noises, the image classification techniques provide poor performance in medical images. Hence, a new classification model for detecting brain tumors is needed to solve the critical challenges in existing approaches. This decreases human intervention while making decisions about brain tumors. Therefore, a new brain tumor classification model is investigated to solve the challenges in existing approaches with deep learning. The proposed framework consists of two main different phases (i) classifying normal and abnormal images and (b) Segmentation and severity classification of abnormal images. The first step is comprised of pre-processing and classification. Firstly, the required images are gathered from the benchmark databases. These collected images are undergone pre-processing, which is done by filtering methods and Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, the pre-processed image is fed into the new classification model as a Transformer-based Multi-scale Residual Attention Network (TMRAN), where the normal and abnormal images are obtained. While in the second case, the classified abnormal image is considered as the input for the segmentation process. The segmented image is obtained by Convolutional Neural Network (CNN), Modified UNet and UNet, in turn the parameters in UNet is optimized by using the Modified Controlling Parameters-based African Vultures Optimization Algorithm (MCP-AVOA), termed as Modified UNet. At the final stage, these segmented images are applied as input to the novel TMRAN model, where the hyper parameters are tuned optimally using improved MCP-AVOA in order to acquire the optimal severity classified results. The performance is validated using diverse measures and compared over other conventional methodologies. Thus, the findings elucidate that the proposed model attains higher classification results.

The human brain is one of the body's most major organs. If there are problems within the human brain, they may cause serious consequences, and even endangers the human life. One of the most fatal diseases for humans is a brain tumor. In the old days, tumor detection was done manually by doctors through reading magnetic resonance images, which might not be time efficient, and sometimes may even produce inaccurate results. Nowadays, with the development of science and technology, Artificial Intelligence (AI) is present in many fields in human life, including medical field. Tumor detection with AI is one of the applications that technology changes human life. The Convolution Neural Network (CNN), a prominent algorithm in deep learning, is widely employed in tumor identification. In this study, a CNN model is proposed. Over 7000 brain tumor magnetic resonance images, including glioma, meningioma, no tumor and pituitary are used in this study. The images are also preprocessed to improve the accuracy of the proposed models. In this study, the well-known VGG16 model, which is a pretrained deep learning model, is utilized to compare with the proposed model. The proposed model and the VGG16 model are trained and evaluated using both the original (uncropped) images and preprocessed (cropped) images. The results of the experiment indicate that the suggested model exceeds the VGG16 model in the light of loss and accuracy.

BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.

Segmentation and classification of brain tumor are time-consuming and challenging chore in clinical image processing. Magnetic Resonance Imaging (MRI) offers more information related to human soft tissues that assists in diagnosing brain tumor. Precise segmentation of the MRI images is vital to diagnose brain tumor by means of computer-aided medical tools. Afterwards suitable segmentation of MRI brain tumor images, tumor classification is performed that is a hard chore owing to complications. Therefore, Gannet Aquila Optimization Algorithm_deep maxout network (GAOA_DMN) and GAOA_K-Net+speech enhancement generative adversarial network (GAOA_K-Net+Segan) is presented for classification and segmentation of brain tumor utilizing MRI images. Here, pre-processing phase performs noise removal from input image utilizing the Laplacian filter and also the region of interest (ROI) extraction is also carried out. Then, segmentation of brain tumor is conducted by K-Net+Segan, which is combined by Motyka similarity. However, K-Net+Segan for segmentation is trained by GAOA that is an amalgamation of Gannet Optimization Algorithm (GOA) and Aquila Optimizer (AO). From segmented image, features are extracted for performing classification phase. At last, brain tumor classification is conducted by DMN, which is tuned by GAOA and thus, output is obtained. Furthermore, GAOA_K-Net+Segan obtained better outcomes in terms of segmentation accuracy whereas devised GAOA_DMN achieved maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 92.7%, 94.5% and 91.5%.

A brain tumor is one of the most prevalent causes of cancer death. The best strategy is the timely treatment of brain tumors in their early detection. Magnetic Resonance Imaging (MRI) is a standard non-invasive method to detect brain tumors. For early detection and better patient survival through MRI scans, the diagnosis needs a high level of knowledge in the radiological and neurological domains to identify the cancers. Researchers have suggested various brain cancer detection techniques. However, most existing automatic cancer detection approaches suffer from poor accuracy and low detection rates. This paper proposes a hybrid deep learning (DL) using deep feature extraction and adaptive Mask Region-based Convolutional Neural Networks (Mask-RCNNs) model for brain tumor detection and classification method to overcome these issues. The experimental findings on the benchmark dataset demonstrate that the planned model is highly effective, with 99.64% accuracy, 95.93% precision, 95.39% recall, and 95.67% F1-score.