Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

A survey on brain tumor diagnosis and edema detection based on machine learning

Early brain tumor diagnosis has a significant role in reducing the risk of disease, as well as led to get better treatment results. Usually, magnetic resonance imaging (MRI) images are evaluated manually through visual inspection, which is difficult, time-consuming and often erroneous;this process is performed by radiologists or clinical experts, and its accuracy depends on their experience. Recently, computer-aided diagnosis (CAD) becomes very essential to overcome these limitations. This paper provides a comprehensive assessment of the existing techniques and methodologies for automated detection of brain tumor coupled with oedema detection methods utilisation, with an emphasis on machine learning models. Moreover, this paper provides an analysis of the integrated procedure that pertains to the retrieval of brain pictures by identifying particular data sets in the procedure to recognise the stipulated attributes

Brain Tumor Boundary Segmentation of MR Imaging using Spatial Domain Image Processing

Extracting information for medical purposes from magnetic resonance imaging is critically important for diagnostic and treatment plans. In this paper, a simple algorithm for tumor segmentation of Magnetic resonance imaging (MRI) is introduced. The novelty incorporates, preserving fine details of the input image while detecting the boundary accurately. Tumor segmentation is carried out by set of pre processing steps followed by morphological operations. Rough contour of the tumor is localized to reduce the search space for the boundary. Line drawing algorithm in cooperated with pixel selection criteria is used to detect the accurate boundary. The algorithm is evaluated in terms of the performance and accuracy with radiologist labelled ground truth MRI scans. Simulation results show that the proposed algorithm provides better identification with above 95% of accuracy, for clearly distinguishable tumors in relation to conventional contour detection methods

An Automated Abnormality Diagnosis and Classi?cation in Brain MRI using Deep Learning

A technique for recognising and labeling malignant brain tissues according to the types of tumours present is known as tumour classification. Magnetic resonance imaging (MRI) can be used in clinical settings to both diagnose and treat gliomas. For clinical diagnosis and treatment planning, the ability to correctly diagnose a brain tumour from MRI images is essential. Manual classification, however, is not feasible in a timely manner due to the enormous volume of data produced by MRI. For classification and segmentation, it is required to employ automated algorithms. However, the numerous spatial and anatomical differences present in brain tumours make MRI image segmentation challenging. We have created a unique CNN architecture for classifying three different types of brain cancers. The new network was demonstrated to be more straightforward than earlier networks using MRI images with contrast-enhanced T1 pictures. Two 10-fold cross-validation techniques, two datasets, and an evaluation of the network's performance were used. A piece of upgraded picture information is used to assess the transferability of the network as part of the subject-cross-validation process. When used for record-wise cross-validation, this method of tenfold cross-validation ground set has an accuracy rate of 92.65 percent. Radiologists who operate in the ground of medical diagnostics may find the newly proposed CNN architecture to be a helpful decision-support tool due to its new transferability capability and speedy execution.

Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

The brain, as the central nervous system's most critical part, can develop abnormal growths of cells known as tumors. Cancer is the term used to describe malignant tumors. Medical imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), are commonly used to detect cancerous regions in the brain. Other techniques, such as positron emission tomography (PET), cerebral arteriography, lumbar puncture, and molecular testing, are also utilized for brain tumor detection. MRI scans provide detailed information concerning delicate tissue, which aids in diagnosing brain tumors. MRI scan images are analyzed to assess the disease condition objectively. The proposed system aims to identify abnormal brain images from MRI scans accurately. The segmented mask can estimate the tumor's density, which is helpful in therapy. Deep learning techniques are employed to automatically extract features and detect abnormalities from MRI images. The proposed system utilizes a convolutional neural network (CNN), a popular deep learning technique, to analyze MRI images and identify abnormal brain scans with high accuracy. The system's training process involves feeding the CNN with large datasets of normal and abnormal MRI images to learn how to differentiate between the two. During testing, the system classifies MRI images as either normal or abnormal based on the learned features. The system's ability to accurately identify abnormal brain scans can aid medical practitioners in making informed decisions and providing better patient care. Additionally, the system's ability to estimate tumor density from the segmented mask provides additional information to guide therapy. The proposed system offers a promising solution for improving the accuracy and efficiency of brain tumor detection from MRI images, which is critical for early detection and treatment

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201