A Comparative Study for Evaluating the Performance of Various Classification Techniques on Brain Tumour

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model baseof the CDSS.

Image processing is a method for enhancing unprocessed images from cameras on aircraft, spacecraft, and satellites as well as images taken regularly for a variety of uses. In general, the following strategies can be used to categorize all image processing operations: Images are represented in several ways, which are referred to as image representation, image preprocessing, image enhancement, image restoration, image analysis, picture reconstruction, and image data compression. The first radiometric normalization, geometric distortion correction, and noise removal of the raw image data been discussed in the past. The goal of the information extraction procedures is to automate the identification of tone in a scene by replacing the visual examination of image data with quantitative techniques. This entails analyzing multispectral image data and establishing the earth cover identification of each pixel in an image using statistically based decision procedures. The goal of the classification procedure is to sort all of the pixels in a digital image into one of several different earth cover classes or themes. The purpose of this study is to examine various image processing approaches and algorithms, many sorts of image processing algorithms: Optical Character Recognition (OCR) and Supporting Vector Machine (SVM) a feature extraction technique, on the vehicle classification dataset and had accurate results of 90% for SVM and 95% for OCR, to further improve the performance of machine algorithms in terms of accuracy for image processing technique using a vehicle. This study can be used for vehicle classification research, it also advances and improves the performance of the system in terms of accurate detection.

Broad ion beam - scanning electron microscopy (BIB-SEM) is an important technology for quantitative description of pore space and detailed investigation of pore structure in mudstones and shales. To obtain quantitative data from BIB-SEM images, image processing is required. However, the effect of different image processing approaches on the segmentation results has not been well documented yet. Particularly the interpretation of pore boundaries approaching the maximum image resolution can represent a major challenge. This study addresses the variability in image processing by comparing conventional thresholding with advanced machine learning methods and analysing the sensitivity of results to different segmentation. The test dataset consists of large area BIB-SEM maps of 12 Middle Miocene mudstone samples from the Vienna Basin, Austria. Segmentation of pore space and organic matter was performed using the thresholding method in ImageJ and the machine learning-based pixel classification in ilastik. The results show that the selected greyscale thresholding algorithm in ImageJ significantly affected the resolved porosity (1.7% to 2.8% variation) relative to the average total porosity of 4.5%. Compared to ImageJ, ilastik provides comparable results with better efficiency in separating pores from organic matter. However, the obtained pore information is inevitably influenced by variations in boundary delineation and feature identification due to inherent differences in the image processing methods and varying representativeness of image maps themselves. Overall, the study findings highlight the importance of carefully selecting image processing methods and considering their potential impact on the accuracy and reliability of pore characterization in mudstones and shales.

Predictive selection rule of favourable image processing methods for X-ray micro-computed tomography images of tablets

CAIMAN brothers: A family of powerful classification and class modeling techniques

The biomedical imaging and image processing course in Shanghai Jiao Tong University is a core course in the Department of Biomedical Engineering. We have taken many measures in the course system, student practice, classroom teaching, and active and creative learning. The effectiveness of the measures and the results are reported. The course is highly appraised by teachers and students and won many awards. The measures and experience of our course may enrich the teaching and learning method bank in Biomedical Engineering (BME) education.

In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches

Image Denoising and Image Segmentation are the two major areas of the medical image processing. The main objective of this paper is to develop a robust segmentation algorithm inorder to detect tumor in 2D MRI brain images. Here we use image denoising as the preprocessing step as noise plays an important role incase of accuracy of affected area of the image, especially in medical diagnostics. To denoise the image, fourth order partial differential equation is employed. A seeded region growing segmentation is used to detect the tumor in MRI brain image. Also skull removal procedure is employed using morphological operators to increase the accuracy of brain tumor detection. This method detects the tumor in the brain image efficiently and also tested for several brain tumor images.

—Brain tumor detection is a critical aspect of neurooncology, where timely and accurate diagnosis significantly impacts patient outcomes. Traditional imaging techniques, while useful, are often limited by variations in image quality, illumination inconsistencies, and the complex nature of brain tumors themselves, which vary widely in size, shape, and texture. This paper presents a deep learning- based approach to brain tumor detection using Convolutional Neural Networks (CNNs) combined with advanced image processing methods. Our methodology involves preprocessing brain MRI scans using histogram equalization, morphological operations, and data augmentation to address illumination issues and enhance the tumor region. Following preprocessing, the images are fed into a CNN, where transfer learning from the Sequential model is applied to improve classification accuracy. Experimental results demonstrate high recall and precision rates, affirming the model’s robustness in detecting brain tumors in MRI images. This approach not only addresses the challenge of inconsistent MRI scan quality but also reduces the risk of misclassification, showing promise for clinical application. Keywords— Brain Tumor Detection, Computer-aided Diagnosis, Computer Vision, Convolutional Neural Networks, Deep Learning, Image Processing, Transfer Learning.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading

Abstract: A brain tumour is described by the presence of abnormal cells in the brain's tissues. Brain tumours can be benign (not cancerous) or malignant (cancerous). The malignant brain tumour is one of the leading and common cancers in the world. There are two types of tumours, primary tumours that develop in the brain and secondary tumours that start in another region of the body and then spread to the brain. The precise identification of the size and location of a brain tumour is crucial in the diagnosis of a brain tumour and is often diagnosed with magnetic resonance imaging (MRI). This book chapter discusses the major types of brain tumours and the advancements in computeraided approaches for detecting brain tumours. The manuscript gives an overview of various recent machine learning and medical image processing approaches developed recently for the identification and classification of brain tumours. Several medical image dataset available for the research works in this domain is also briefed in this article. The major research challenges which can be addressed by the researchers in the domain of brain tumour detection are also discussed in this article. Method: This manuscript mainly discusses various medical image processing methods to detect brain tumors. Results: The manuscript discusses the efficiency of the existing schemes and the key areas where further improvement is required Conclusion: This manuscript gives an overview of various categories of brain tumors, existing methods to detect brain tumors, the critical challenges in this domain and the medical image dataset available for the study.

Cardiovascular Disease (CVD) is one of the primary diseases that causes death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, every 34 seconds, a person dies due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. The main aim of this work is to improve the performance of the heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. The objective of this investigation is to diagnose heart diseases using feature extraction, reduction based classification and image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system includes three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In the second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image are calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image are given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image by determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with a regression nature. Due to the regression property, the network is well trained with the feature. The Generalized regression neural network is used for classifying heart diseases. The proposed method achieves the accuracy of 96.23%, sensitivity of 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods, either the feature extraction based classification or the feature reduction based classification.

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.