Image Processing with the Specific Focus on Early Tumor Detection

Among the list of dreadful diseases, 'Brain Tumor and Brain Cancer' is one of them. According to survival rate, after the detection of cancerous Brain or Central Nervous System (CNS) tumor, 34% of men and 36% of women survive at least 5 years. Therefore, 'Early Detection of tumor' can be a savior for so many lives and artificial intelligence will make the way very accurate and appropriate. A new non-invasive approach to detect brain tumors in early-stage, cost-efficiently with the lowest risk factor is-'Liquid Biopsy'. It is a technique for analyzing non-solid biological tissue, i.e. - blood, plasma, cerebrospinal fluid (CSF). As human inspection maybe error full and inaccurate, in this paper, we have used 'k-means clustering' in 'Image Processing' as visual perception of artificial intelligence for detection of 'CTC' and we have used the technique 'Convolutional Neural Network' (CNN) for ctDNA detection and 'Support Vector Machine'(SVM) algorithm for classification of ctDNA and normal cell-free DNA (cfDNA). 'ctDNA' could provide clues for growth factor of cancerous cells, type of tumors, location, etc. Therefore, our work provides a new way to early detection and a new prospect for early cure.

The diagnosis of cancer can be challenging and time-consuming due to the complex characteristics of tumors and inherent noise in medical imaging. The significance of early detection and localization of tumors must be considered. Radiological imaging techniques can detect and potentially forecast the presence of neoplastic growths at various phases. The expeditiousness of the diagnosis process can be notably enhanced by amalgamating these images with algorithms designed for segmentation and relegation. Early detection of tumors and accurate localization of their position are critical factors. Medical scans, when used with segmentation and relegation procedures, enable the prompt and precise detection of cancerous tumor regions. The identification of malignant tumors enables this achievement. The present article introduces a framework for detecting brain tumors based on a convolutional neural network (CNN). The initial step in processing brain magnetic resonance imaging (MRI) images involves the application of a Gaussian filter to eliminate any noise present. Subsequently, CNN and long short-term memory (LSTM) deep learning methodologies are employed to classify images. CNN has demonstrated improved accuracy in the classification and detection of brain tumors. CNN has achieved an accuracy of 99.25% in cancer image classification. The sensitivity and specificity of CNN are also 98.75% and 99.25%, respectively.

Brain tumor disease is a serious and complex health problem worldwide. Early and accurate detection of brain tumors has a major impact on patient care and prognosis. Magnetic Resonance Imaging (MRI) has become one of the main diagnostic tools in detecting brain tumors, manual interpretation of MRI images requires high clinical expertise and requires a long time. In recent years, advances in deep learning techniques and image processing have opened up new opportunities in the detection of brain tumors via MRI images. Deep learning techniques, especially the use of Vision Transformers (ViTs) models, have been successful in various complex pattern recognition tasks in images. The Vision Transformers model was chosen due to the performance improvements shown in many image recognition tasks, outperforming convolutional neural networks (CNN) based methods. Tensorflow and Keras are used as frameworks for development and training models, which have been proven effective and efficient in various previous studies. This study focuses on the performance of the Vision Transformer (ViT) in detecting brain tumors through two Magnetic Resonance Imaging (MRI) image datasets, with different numbers of datasets, as well as the maximum accuracy value that can be achieved from the ViT architecture. From several experimental parameters on ViT, the number of datasets and iterations, the results obtained from the first dataset with 253 image data obtained an accuracy value of 88%, and in the second study by combining the two datasets, with 3.123 data images obtained an accuracy of 97.9%.

Multi-task Contexture Learning Network for automated vertebrae segmentation and tumor diagnosis from MRI

This paper examines the articulatory correlates of the Hero and Villain Voice Types, which were auditorily identified in a separate study on cartoon voices, using the magnetic resonance imaging (MRI) technique. In general, the MRI images were in good agreement with the previous auditory analysis results; the major characteristic difference between voice quality settings of heroes and those of villains and between two villainous voice types was found in the supraglottal states and the pharyngeal cavity. Auditory analysis can be as valid as acoustic or any other analysis method depending on the level of training in a commonly accepted system such as Laver’s framework for voice quality description. However, the MRI technique also allowed us to see what would not be observed otherwise, e.g., larynx height, pharyngeal cavity, vocal tract length, and the position of the hyoid bone. Auditory and physiological methods should be used in combination in order to further our understanding of the larynx and the pharynx.

Early and accurate detection of brain tumors in Magnetic Resonance Imaging (MRI) scans is essential for effective treatment and improved patient survival. Although MRI is a widely used diagnostic tool, the manual interpretation of these images is often time-consuming and prone to variability among medical professionals. In biomedical imaging, deep learning methods have been increasingly explored to address these challenges, particularly for automated tumor detection and segmentation. This study compares three advanced object detection models—YOLO-v9, YOLO-v10, and YOLO-v11—evaluated on a dedicated brain tumor MRI dataset. Performance was assessed using multiple metrics, including precision, recall, F1-score, Intersection over Union (IoU), and mean Average Precision (mAP) at 0.5:0.95. In addition, Non-Maximum Suppression (NMS) and frames-per-second (FPS) rates were examined to determine efficiency and feasibility for real-time inference. The results demonstrate that all three YOLO-based architectures achieve high accuracy and efficiency, highlighting their suitability for medical image analysis. Among them, YOLO-v10 achieved the best balance of performance, recording the highest precision (0.95), a substantial recall value (0.88), and superior mAP scores. These outcomes confirm YOLO-v10’s advantage in providing reliable and consistent detection compared to YOLO-v9 and YOLO-v11. Overall, the findings underscore the potential of advanced YOLO models in supporting clinical decision-making by enabling faster, more precise, and automated brain tumor detection, thereby contributing to improved diagnostic reliability and patient outcomes.

The utilization of image processing tools for magnetic resonance image (MRI) processing has made possible carrying out the study of injuries and diseases, as it facilitates the look up of the information contained in the image. The objective of this work is the implementation of a digital image processing methodology to perform the segmentation of lateral ventricles in MRI images of pediatric patients using automatic thresholding techniques, contour detection, and morphological operators. The segmentation algorithms efficacy is evaluated with the support of a specialist, resulting in an 82% of efficiency in the segmentation of the lateral ventricles. On the other hand, the average processing time is 4.79 seconds. With these results, the study allows establishing the effectiveness of the automatic calculation of thresholds, as well as the use of contour extraction algorithms, and morphological operators as support tools that accentuates the validity of the segmentation model.

Imaging of Multiple Sclerosis: Role in Neurotherapeutics

—Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process. Index Terms — Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter. I. INTRODUCTION A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. Brain tumor has a higher mortality rate when compared to more common types of cancer. This underlines the need of accurate prognosis of brain tumor in health care industry [1]. MRI has become a widely-used method of high-quality medical imaging, especially in brain imaging [2]. Image segmentation refers to the process of partitioning a digital image into multiple segments, also known as super pixels. Image segmentation is an important process in computer vision and image processing, which divides an image into a number of disjoint regions such that the pixels have high similarity in order to use its different fields; science, medical, agriculture and industrial fields [3]. Besides, Magnetic Resonance Imaging (MRI) images are known to be corrupted by noise [4], blur [5] and low contrast artifact [6]. Therefore, this article discusses the issue of achieving a better segmentation through the use of proper image restoration and enhancement techniques. Moreover, the accurate and automatic segmentation of a brain tumor on MRI image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, and constructing tumor growth models [7]. For those reasons, an automatic and reliable tumor segmentation method would be a useful tool since the medical image segmentation plays an instrumental role in clinical diagnosis [8]. The rest of the article sections are organized as the following: in section two, a brief elucidation about some related studies is presented as the used literature review. Moreover, detailed explanations about the employed algorithms are delivered in section three as a methodology. Also, the results of the proposed methodology are illustrated in section four as the experimental results. Likewise, a detailed discussion about the use of certain algorithms and its results is deliberated in section five. Finally, a brief closure about this article is provided in section six as a conclusion. II. LITERATURE REVIEW In this section, three recent studies are briefly described. The first study was presented by [9] which proposed a texture based analysis to identify irregularity in the brain and automatic segmentation using seeded region growing segmentation. The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images. Detecting tumor precisely based on seeded region growing segmentation on an improved single seed point selection algorithm is

Preface

Nuclear magnetic resonance (NMR) and nuclear magnetic resonance imaging (MRI) techniques are described in relation to applications on intact plants. These techniques are non-destructive and non-invasive and can be used to study the dynamics of plant water relations and water transport, e.g., as a function of environmental (stress) conditions. Non-spatially resolved NMR as well as two-dimensional or even three-dimensional imaging strategies can be applied. The relevant NMR parameters that can be used to discriminate between water in sub-cellular compartments are the relaxation times T 1 and T 2, and (restricted) diffusion. The information within MRI images can be manipulated in such a way that in addition to anatomical information physico-chemical information like cell compartment size, water membrane permeability, water diffusion and flow, information on chemical composition (water, oils, sugars and other metabolites) are obtained. This information is also available in non-spatially resolved NMR with a much higher signal-to-noise ratio, but the interpretation of such non-spatially resolved measurements can be hampered by heterogeneity of the plant tissue (sub-cellular compartments, tissue heterogeneity and different types of tissue). Here we present a number of 1H NMR and MRI techniques that can be used to study plant performance in relation to its photosynthetic activity. In addition, dedicated hardware for intact plant studies is discussed.

Brain tumors are among the main causes of cancer-related mortality in humans. Early detection of brain tumors is a vital job in the medical task of diagnosis and cure planning for patients. The automatic detection greatly facilitates medical personnel. Magnetic resonance imaging (MRI) is an accepted imaging strategy for diagnosing brain tumors. Presently, deep learning approaches have proven effective in handling various computer vision problems, such as image classification, because of their high performance and also determine models that can learn and decide based on sample data. In this study, the deep transfer learning method, namely InceptionResNet-V2, ResNet50, MobileNet-V2, and VGG16, was used to compare and find the most suitable model for brain tumor detection from the public MRI dataset. Also, CLAHE was employed as an image enhancement technique to improve the quality of the image data set before being used as the model input. As a result, the suggested method performed a prediction accuracy of up to 100%.

Brain tumors are abnormal tissue growth characterized by uncontrolled and rapid cell proliferation. Early detection of brain tumors is critical for improving patient outcomes, and magnetic resonance imaging (MRI) has become the most widely used modality for diagnosis due to its superior image quality and non-invasive nature. Deep learning, a subset of artificial intelligence, has revolutionized automated medical image analysis by enabling highly accurate and efficient classification tasks. The objective of this study is to develop a robust and effective brain tumor detection system using MRI images through transfer learning. A diagnostic framework is constructed based on convolutional neural networks (CNN), integrating both a custom sequential CNN model and pretrained architectures, namely VGG16 and EfficientNetB4, trained on the ImageNet dataset. Prior to model training, image preprocessing techniques are applied to enhance feature extraction and overall model performance. This research addresses the common challenge of limited MRI datasets by combining EfficientNetB4 with targeted preprocessing, data augmentation, and an appropriate optimizer selection strategy. The proposed methodology significantly reduces overfitting, improves classification accuracy on small datasets, and remains computationally efficient. Unlike previous studies that focus solely on CNN or VGG16 architectures, this work systematically compares multiple transfer learning models and demonstrates the superiority of EfficientNetB4. Experimental results on the Br35H dataset show that EfficientNetB4, combined with the ADAM optimizer, achieves outstanding performance with an accuracy of 99.66%, precision of 99.68%, and an F1-score of 100%. The findings confirm that integrating EfficientNetB4 with dataset-specific preprocessing and transfer learning provides a highly accurate and cost-effective solution for brain tumor classification, facilitating rapid and reliable medical diagnosis.

Introduction Relaxometry images are an important magnetic resonance imaging (MRI) technique in the clinical routine. Many diagnoses are based on the relaxometry maps to infer abnormal state in the tissue characteristic relaxation constant. In order to study the performance of these image processing approaches, a controlled simulated environment is necessary. However, a simulated relaxometry image tool is still lacking. This study proposes a computational anatomical brain phantom for MRI relaxometry images, which aims to offer an easy and flexible toolkit to test different image processing techniques, applied to MRI relaxometry maps in a controlled simulated environment. Methods A pipeline of image processing techniques such as brain extraction, image segmentation, normalization to a common space and signal relaxation decay simulation, were applied to a brain structural ICBM brain template, on both T1 and T2 weighted images, in order to simulate a volumetric brain relaxometry phantom. The FMRIB Software Library (FSL) toolkits were used here as the base image processing needed to all the relaxometry reconstruction. Results All the image processing procedures are performed using automatic algorithms. In addition, different artefact levels can be set from different sources such as Rician noise and radio-frequency inhomogeneity noises. Conclusion The main goal of this project is to help researchers in their future image processing analysis involving MRI relaxometry images, offering reliable and robust brain relaxometry simulation modelling. Furthermore, the entire pipeline is open-source, which provides a wide collaboration between researchers who may want to improve the software and its functionality.

Standard magnetic resonance imaging (MRI) techniques failed to image adequately acute hemorrhagic transformation (HT). Therefore, computed tomography (CT) is still needed to exclude intracerebral hemorrhage. New MRI techniques such as diffusion- and perfusion-weighted imaging (DWI and PWI) may improve the early detection of HT. The utility of this approach requires a direct comparison of the sensitivity of CT with these MRI techniques. Methods: Nine patients experienced an acute carotid artery territory ischemic stroke diagnosed on a first CT performed 3.8 ± 2 h after the onset of stroke. They underwent a second CT 12 ± 4 h after the onset of stroke, followed 35 ± 10 min later by an MRI protocol including: (1) an axial isotropic DWI SE echo-planar imaging (EPI) sequence; (2) time of flight MR angiography (TOF MRA); (3) PWI with an axial T₂*-weighted gradient echo EPI sequence using 20 ml gadolinium contrast agent (Gd-DTPA); HT was characterized on DWI SE EPI as a heterogeneous area of signal loss within the ischemic area; (4) at day 7, CT was also performed in all patients who had an early suspicion of bleeding according to MRI. Results: An HT was detected exclusively with CT in 1 out of 9 patients, while an MRI pattern of HT was found in 6 out of 9 patients. In 5 of these 6 patients, the CT scan did not show an obvious pattern of HT. Day 7 CT confirmed HT in all patients who had early suspicion of bleeding according to DWI criteria. Conclusion: This study suggests that new MRI techniques may allow an early detection of HT, thus improving the management of stroke.