The human brain is among the body's most complicated and imperative organs, governing the neurological system. The most deadly brain tumor is caused by erratic and out-of-control cell growth in the brain [1]. Patient Survival depends on the type of glioma type; lowgrade gliomas have survival rates of 5 years as high as 80%, whereas survival rates of 5 years are under 5% for high-grade gliomas [2]. Timely brain tumor recognition and categorization is an imperative research topic in the clinical imaging domain, and it assists in choosing the most suitable treatment plan for a patient's life-saving [3].
Several screening methods, either invasive or non-invasive, are employed to identify tumors in the human brain [4]. Magnetic resonance imaging (MRI) is a preferable, less harmful scanning modality since it provides rich information about the location of brain tumors, shape, and size in medical images (MI) and is generally considered quicker, cheaper, and safer [5]. Manual assessment of brain MR scans is challenging for radiologists to identify and categorize brain tumors from MIs. A computer-aided diagnosis (CADx) is required to reduce the burden and assist radiologists or doctors with MI assessment.
Many research areas are being explored in medical image analysis. It includes medical imaging domains like identification, detection, and segmentation [6]–[11]. Traditional ML approaches comprise numerous steps, pre-processing, feature extraction and selection, and classification. More discriminative feature acquisition is essential, as classification accuracy relies on obtained features [12].
In conclusion, the conventional ML techniques have two key challenges in the feature extraction step. One, it solely emphasizes low- or high-level attributes. Secondly, standard ML techniques rely on hand-crafted features that require significant prior knowledge, such as the position of the tumour in a medical scan, However, there is a considerable risk of human error. Designing an effective system to incorporate high- and low-level features with no human intervention is crucial. As brain tumor datasets are being expanded, there is a need for technological improvements in feature extraction focusing on confined and imbalanced MR imaging data sets of brain abnormalities and other irregularities of the human organs[13] [14].
Recently, deep learning (DL) methods have frequently been employed for brain MRI categorization [15]. While feature mining and classification were integrated into self-learning, deep learning methods do not necessitate a manual process for feature extraction. The DL approach requires a dataset, and minimal pre-processing is required for selecting salient features in a self-learning way [16]. MR imaging categorization faces a significant challenge in diminishing the semantic space among high-level spatial details observed by a human assessor and low-level acquired using the imagery mechanism. One of the well-known neural network models, convolutional neural networks (CNNs), specially designed for images, is utilized for feature extraction to acquire the important characteristics to categorize and minimize the semantic gap [17] .
Recently, in many studies, CNNs have been widely employed to classify brain MRI and validated on a different dataset of brain tumors [18]–[22]. A deep CNN-based model was proposed in [23] for brain MRI images categorization into distinct classes. The authors used brain MRI images from a publicly available dataset to prevent model ambiguity. The suggested model has a classification accuracy of 91.4%. Deepak and Ameer [24] employed a pre-train deep CNN, GoogLeNet, to extract key attributes using brain MR images and classify tumors into three classes with 98% accuracy. Ahmet and Muhammad [25] categorized brain MR images using various CNN models and attained satisfactory accuracy. They modify a pre-trained ResNet-50 DCNN by excluding the final five layers and introducing additional eight layers. The model achieved the highest among all pre-trained models accuracy of 97.2%. Sultan et al. [26] suggested a CNN-based deep learning model utilizing two publicly accessible datasets have 3064 (glioma, meningioma, and pituitary tumors) and 516 (Grade II, Grade III, and Grade IV) brain medical scans. The proposed method has the best accuracy of 96.13% and 98.7%. Khwaldeh et al. [27] used several CNNs to classify brain MRI images and achieved good results. Using modified pre-trained Alexnet CNN, they achieved a higher accuracy of 97.2%. Khan, M.A. et al. [28] developed a multi-model-based technique to differentiate brain tumors with DL. The presented system includes many stages. They used partial least squares (PLS) to concatenate the features and ELM for classification. Their methodology stated improvement of 97.8%, 96.9%, and 92.5% on BraTs-2015, BraTs-2017, and BraTs-2018, respectively. Özyurt et al. [29] presented a technique for detecting brain tumors. They began with MRI tumor image segmentation with the NS-EMFSE algorithm. They obtained features from the segmented image using AlexNet and then using the SVM, they detected and classified brain tumor images as benign or malignant with 95.62% accuracy. However, most of these models are assessed on small-scale datasets due to the inaccessibility of the data repositories. Likewise, the majority of earlier research was based on pre-trained CNN models, which were developed generally for a dataset of natural images. Pretrained models are customized for the brain tumor task without designing them to distinguish brain tumor patterns. Thus it limits the use of pre-trained CNN models for brain tumor diagnosis.
In this study, a new deep CNN-based brain tumor classification scheme is developed for MRI image categorizing. A novel CNN architecture, Res-BRNet, is suggested for brain tumor classification. Performance assessment is performed using standard measures like sensitivity, precision, F1-score, accuracy, and AUC-PR/ROC. Moreover, we have generated a large dataset by collecting brain MRI images of three tumor types and normal brain images from publicly accessible sources. The prediction ability of the developed approach is assessed on the test dataset and assessed with a comparison of numerous existing DCNNs, and also proposed technique's idea is compared with baseline approaches. The proposed work has the following contributions:
-
A new deep residual and regional CNN architecture, Res-BRNet, is developed for brain tumor classification.
-
The developed Res-BRNet employed Regional and boundary-based operations in a systematic order within the modified spatial and residual blocks to exploit spatial correlation information and textural variations from brain tumor MRIs.
-
The systematic integration of residual and spatial blocks within the proposed Res-BRNet CNN improves discriminative capability and generalization. Moreover, spatial blocks extract homogeneity and boundary-defined features at the abstract level. Furthermore, residual blocks at the target level effectively learn local and global texture variations of different brain tumors.
-
The proposed brain tumor Res-BRNet classification CNN significantly reduces false positives and negatives compared to several existing CNN architectures.
The rest of the manuscript is organized as follows: Section 2: incorporates the proposed methodology. The results and discussion are described in section 3, and the last Section 4: concludes the entire paper.