Effective diagnosis of brain tumor classification by using novel DCNN

A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods, Datasets, Libraries, and Future Directions

Brain tumors present a significant health challenge globally, necessitating advanced techniques for accurate detection, segmentation, and classification. This paper presents a comprehensive study focused on the development and evaluation of innovative methodologies for brain tumor analysis using medical imaging data. The primary objective of this research is to enhance the accuracy and efficiency of brain tumor detection, segmentation, and classification processes. To achieve this goal, a multi-step approach is proposed, integrating various computational techniques and machine learning algorithms. First, the study explores novel methods for preprocessing medical imaging data to enhance image quality and reduce noise artifacts. This preprocessing step plays a crucial role in improving the subsequent analysis stages' accuracy and reliability. Next, a robust tumor detection algorithm is developed, leveraging advanced image processing techniques and deep learning models. The proposed algorithm effectively identifies tumor regions within brain images with high accuracy and minimal false positives. Following tumor detection, a segmentation framework is introduced to precisely delineate tumor boundaries from surrounding healthy tissues. The segmentation algorithm combines traditional image processing methods with state-of-the-art deep learning architectures to achieve accurate and efficient tumor delineation. In this paper, we proposed three novel methods for automatic detection, classification, and segmentation of brain tumors. Comprehensive experiments are conducted on the BRATS dataset and show that the proposed model obtains competitive results. The parameters under study are Accuracy rate, Specificity, and Sensitivity. First approach focuses on classifying cancerous and non-cancerous brain tumors in MRI scans. The system first reads the images and employs a novel fusion method to combine information from various modalities (Flair, T1, T1C, T2) for a more comprehensive picture. This enhances the accuracy of tumor characterization. Following this fusion, the images undergo preprocessing, feature extraction, and classification. The preprocessing stage involves grayscale conversion, binarization, wavelet analysis, and region-of-interest (ROI) calculation. Finally, a robust Neural Network (NN) classification method effectively differentiates cancerous and non-cancerous brain tissue. Performance is evaluated by metrics like accuracy, sensitivity, and specificity. Compared to existing methods, this research system demonstrates superior results, achieving a classification accuracy of 96.61%, sensitivity of 96.66%, and specificity of 96.55%. The Second approach combines Backpropagation Neural Networks (BPNN) with Spatial Fuzzy C-Means (SFCM) clustering for brain tumor analysis. The BPNN classifies brains into normal, benign (non-cancerous abnormality), or malignant (cancerous) categories. SFCM helps pinpoint the exact tumor location and size within the MRI scan through segmentation. A dual-tree complex wavelet transform is employed to extract image features efficiently. This method achieved exceptional results: 99.77% classification accuracy, 99.87% sensitivity (correctly identifying tumors), and 98.69% specificity (correctly identifying healthy tissue). Additionally, the over 90% precision demonstrates the effectiveness of this technique in feature extraction and classification. The experiments demonstrate that BPNN-SFCM successfully segment, and extracts brain tumors from MRI scans. The overall accuracy of BPNN-SFCM is better as compared to other proposed methods and existing methods.

Brain tumors are among the most common and fatal types of cancer.Accurate and timely diagnosis of these tumors is essential for disease management and successful patient prognosis.Additionally, the precise identification of the tumor type is crucial in determining the treatment path.By recognizing the type of tumor, doctors can select the most appropriate treatment method, which may include surgery, radiation therapy, chemotherapy, or a combination of these methods.Furthermore, the tumor type helps predict disease progression and the quality of life post-treatment.In recent years, deep learning has been a powerful tool for various image-processing tasks, including brain tumor detection.In this paper, various deep learning models such as CNN, RNN, VGG16, InceptionV3, and ResNet101 are evaluated for classifying brain tumor types from the Figshare dataset based on MRI images of glioma, meningioma, pituitary tumors, and no tumor.Finally, a suitable deep model based on ResNet101 combined with transfer learning is proposed.Based on various metrics and statistical tests, the findings indicate that deep learning models can be effectively used for brain tumor detection.Among these, the ResNet101 model achieved an accuracy of 98.37% in classifying the four tumor classes.This study demonstrates that deep learning holds significant potential for improving brain tumor detection accuracy.

Because brain tumors are complex, they must be diagnosed accurately and early. Brain tumor diagnosis requires expert radiologists and large datasets of annotated images, which is resource-intensive and time-consuming. This study investigates the use of transfer learning techniques to detect and classify brain tumors using MRI images. Using transfer learning, pre-trained models can be tuned to perform specific medical tasks while reducing the impact of large datasets. AlexNet, GoogleNet, ResNet-50, and VGG-16 were compared for classifying gliomas, meningioma’s, and pituitary tumors based on transfer learning models. Based on the findings, the proposed hybrid GN-AlexNet model showed superior accuracy, sensitivity, specificity, and F1 score when compared with all other models, demonstrating its potential for improving brain tumor detection efficiency. The findings of this research pave the way for the adoption of transfer learning in clinical settings, providing medical professionals with more efficient and accessible solutions.

Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement. Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming. Keywords: Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.

The detection of brain tumors using magnetic resonance imaging is currently one of the biggest challenges in artificial intelligence and medical engineering. It is important to identify these brain tumors as early as possible, as they can grow to death. Brain tumors can be classified as benign or malignant. Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee treatment throughout recovery. In this study, a comprehensive approach to diagnosing benign and malignant brain tumors is proposed. The proposed method consists of four parts: image enhancement to reduce noise and unify image size, contrast, and brightness, image segmentation based on morphological operators, feature extraction operations including size reduction and selection of features based on the fractal model, and eventually, feature improvement according to segmentation and selection of optimal class with a fuzzy deep convolutional neural network. The BraTS data set is used as magnetic resonance imaging data in experimental results. A series of evaluation criteria is also compared with previous methods, where the accuracy of the proposed method is 98.68%, which has significant results.

Brain Tumor is a deadly diagnosis that, if not detected early on, can cause significant damage to a patient’s brain, disrupting the body’s functions and even leading to death. However, the conventional method of detecting brain tumors is conducting MRI scans which a medical expert then consults for diagnosis. While time-consuming, this method also leaves room for human error, especially in cases where the tumor is in its early stages. Thus, the diagnosis of brain tumors must be made accurately in the least time possible. This paper aims to prevent premature mortality, provide health in resource-constrained settings, and seek patients’ healthy lives, which can be done through timely and accurate diagnosis of brain tumors. In this paper, three different deep transfer learning models are used after adding fine-tuned layers to detect brain tumors in a dataset of 251 Magnetic Resonance Imaging (MRI) scans. The transfer learning models used are VGG16, InceptionV3, and ResNet50, with fine-tuned Dropout, Flatten, and Dense added layers. The highest accuracy was achieved with the VGG16 model, which had an accuracy of 91.58%. Thus, deep learning models are proven effective in detecting brain tumors without the unwanted expense of time and resources.

ObjectiveThe primary objective of this study is to propose a novel Brain-tumor Detection Network (BTDN) for MRI-based brain tumor diagnosis. The method aims to enhance image quality, ensure secure data transmission, and achieve highly accurate classification of brain tumors while addressing challenges related to manual interpretation and data security.DatasetThe study utilizes three publicly available MRI datasets to evaluate the proposed method. The first dataset, D-I (Br35Hc), comprises brain MRI images specifically curated for tumor classification tasks. The second dataset, D-II (BraTS), is the widely recognized Brain Tumor Segmentation Dataset, frequently used for brain tumor detection and segmentation. Lastly, D-III (Kaggle Data Repository) is a collection of brain tumor MRI images obtained from the Kaggle platform, further diversifying the data sources for performance evaluation.MethodThe proposed methodology involves several key components to enhance the accuracy and security of brain tumor classification. During preprocessing, Contrast Limited Adaptive Histogram Equalization is employed to improve image quality by enhancing contrast, while data augmentation ensures the durability and robustness of the dataset during training. The core of the approach is the BTDN, specifically developed for precise classification of brain tumors. To address data security concerns, the study introduces Secure-Net (SN), which prevents data modification and enables safe retrieval using specific identifiers for trusted information exchange. The performance of BTDN is rigorously evaluated by comparing it against six widely used deep learning models: ResNet101, DenseNet169, VGG19, MobileNetV3, InceptionV3, AlexNet, and ConvNeXt.ResultsThe BTDN demonstrated outstanding classification accuracies across the three datasets, achieving 99.68% on D-I (Br35Hc), 98.81% on D-II (BraTS), and 95.33% on D-III (Kaggle). These results surpass the performance of the compared deep learning models, underscoring the effectiveness of BTDN for accurate and reliable brain tumor detection and classification.ConclusionThe study demonstrates that the proposed BTDN model ensures high accuracy in MRI-based brain tumor classification while incorporating SN to enhance data security and safe transmission. The remarkable performance across multiple datasets underlines BTDN’s potential as a reliable tool for precise and secure brain tumor diagnosis in clinical settings.

To detect a brain tumor and its growth from MRI of brain image is one of the most common techniques in medical research field. From the internal structure of human brain, the scanning process of brain image gives more detailed information about the growth of brain tumor. Despite many years of research and substantial contributions, brain MRI segmentation is still a very challenging task to suit for range of diagnosis. It has been generalized to detect the brain tumor manually from medical image, which is a complex and tedious task. Even though several methods and encouraging results are obtained for brain tumor segmentation, sensitive and accurate detection are still a thought-provoking task due to the different shapes, locations and image intensities of different types of tumor. In this paper, a comprehensive survey on the brain tumor detection from MRI images is presented. The paper draws an attention towards the background of brain tumor and its distinction, discussion on brain tumor segmentation and algorithms classification. Finally, state-of-the-art-techniques for brain tumor detection are discussed based on the recent works and the review is concluded with open issues and challenges. This paper not only reviews, compares and consolidates the recent related works, but also admires the author's findings, solutions and discusses its usefulness towards tumor detection in medical applications. The uniqueness of this paper lies in the review of brain tumor segmentation and detection using MRI images.

In this manuscript, an efficient framework is proposed for brain tumour classification (BTC) based on hierarchical deep-learning neural network (HieDNN) classifier. Here, the input images are collected from Brats dataset. The input images are preprocessed using Savitzky-Golay denoising method to decrease the noise. The image features, like texture features, are removed using grey-level co-occurrence matrix (GLCM). The extracted images are fed to Hie DNN classifier, which is helpful for classifying the brain images. The proposed system is implemented in MATLAB. The proposed method attains higher accuracy of 31.14%, 16.09% and 11.48% during benign; during malignant higher accuracy 35.18%, 19.17% and 22.80%; during normal higher accuracy 44.20%, 29.97% and 20.44% compared with the existing methods, like convolutional neural network for BTC depending on MRI images (CNN-BTC), microscopic brain tumour detection with classification utilising 3D CNN and feature selection architecture (3DCNN-BTC), BTC utilising hybrid deep auto-encoder using Bayesian fuzzy clustering-based segmentation methodology (DAEN-BTC), respectively.

Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on biopsy measures and involves human subjectivity. The use of automated CAD techniques for brain tumor detection and classification can overcome these drawbacks. The paper aims to create two deep learning-based CAD frameworks for automatic detection and severity grading of brain tumors - the first model for brain tumor detection in brain MR images and model 2 for the classification of tumors into three types: Glioma, Meningioma, and Pituitary based on severity grading. The novelty of the research work includes the architectural design of deep learning frameworks for detection and classification of brain tumor using brain MR images. The hyperparameter tuning of the proposed models is done to achieve the optimal parameters that result in maximizing the models' performance and minimizing losses. The proposed CNN models outperform the existing state of the art models in terms of accuracy and complexity of the models. The proposed model developed for detection of brain tumors achieved an accuracy of 98.56% and CNN Model developed for severity grading of brain tumor achieved an accuracy of 92.36% on BraTs dataset. The proposed models have an edge over the existing CNN models in terms of less complexity of the structure and appreciable accuracy with low training and test errors. The proposed CNN Models can be employed for clinical diagnostic purposes to aid the medical fraternity in validating their initial screening for brain tumor detection and its multi-classification.

Recent advances in deep learning techniques have made medical analysis available with enhanced accuracy and efficiency, where brain tumor classification is automatically identified in an influential role. Hence, one of the synthesized approaches of an innovative idea to use GANs in this paper development is the synthesis of T1-weighted and post-contrast ischemic stroke brain MRIs to increase performance in the classification of the mentioned diseases according to deep learning. This paper, therefore, has the following objective: to evaluate the efficiency of GAN-generated images in learning deep in the transfer learning models and the performance in both tumor and non-tumor brain images. We use the two main architectures of GAN in our process: Vanilla and Deep Convolutional GAN (DCGAN). Details of the three major deep transfer learning models below portray the Convolutional Neural Network (CNN), MobileNetV2, and ResNet152v2. This learned weight would become a pre-trained representation of the models combined with the augmented dataset for feature extraction and classification purposes. I.e., where transfer learning is applied in the models, it is way more accessible for those architectures of the neural network to tap into the knowledge learned by the former from large-scale datasets and adapt it for tasks at hand on classifying brain tumors. Concerning training and validation, Python programming language integrated with the Keras deep learning framework was employed to implement the indicated operations. In terms of training, GPU processing power was available to allow the model to learn faster. In this regard, this was incorporated with the GPU processing by using the NVIDIA GeForce RTX 2060 GPU. Both Vanilla GAN and DCGAN have counterparts when generating images.

A brain tumor can develop due to uncontrolled proliferation of aberrant cells in brain tissue. Malignant tumor can influence the nearby brain tissues, potentially resulting in the person's death. Early diagnosis of a brain tumor is crucial for ensuring the survival of patients. This article introduces an improved method using a deep auto encoder for the classification and detection of brain tumor. Magnetic resonance imaging (MRI) images are obtained from the BraTS data sets. The images undergo preprocessing using an adaptive Wiener filter. Image preprocessing is essential for eliminating noise from the input MRI pictures, hence enhancing the accuracy of MRI image classification. The fuzzy C-means technique is used to accomplish image segmentation. The classification model comprises deep auto encoder, convolution neural network (CNN), and K-nearest neighbor techniques. The classification model is developed and evaluated using MRI image slices from the BraTS dataset. Accuracy of deep auto encoder is 98.81%. Accuracy of CNN is 95.50 and accuracy of K-nearest neighbor (KNN) technique is 91.30%.

Brain tumors are severe medical conditions that can prove fatal if not detected and treated early. Radiologists often use MRI and CT scan imaging to diagnose brain tumors early. However, a shortage of skilled radiologists to analyze medical images can be problematic in low-resource healthcare settings. To overcome this issue, deep learning-based automatic analysis of medical images can be an effective tool for assistive diagnosis. Conventional methods generally focus on developing specialized algorithms to address a single aspect, such as segmentation, classification, or localization of brain tumors. In this work, a novel multi-task network was proposed, modified from the conventional VGG16, along with a U-Net variant concatenation, that can simultaneously achieve segmentation, classification, and localization using the same architecture. We trained the classification branch using the Brain Tumor MRI Dataset, and the segmentation branch using a “Brain Tumor Segmentation dataset. The integration of our method’s output can aid in simultaneous classification, segmentation, and localization of four types of brain tumors in MRI scans. The proposed multi-task framework achieved 97% accuracy in classification and a dice similarity score of 0.86 for segmentation. In addition, the method shows higher computational efficiency compared to existing methods. Our method can be a promising tool for assistive diagnosis in low-resource healthcare settings where skilled radiologists are scarce.

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.