Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach

In an abnormal tissue called a brain tumor, the cells of the tumor reproduce quickly. if no control over tumor cell growth. The difficulties involved in identifying and treating brain tumors Machine learning is the most technologically sophisticated tool for classification and detection, implementing reliable state-of-the-art A.I. as well as neural network classification techniques, the use of this technology in early diagnosis detection of brain tumors can be accomplished successfully. it is well known that the segmentation method is capable of helping simply destroy the brain's abnormal tumor regions In order to segment and categorize brain tumors, this study suggests a multimodal approach involving machine learning and medical assistance. Noise can be seen in MRI images. To make the method for eliminating noise from images easier, a geometric mean is used later. The algorithms used to segment an image into smaller pieces are fuzzy c-means algorithms. Detection of a specific area of interest is made simpler by segmentation. The dimension reduction procedure is carried out using the GLCM. Photographic features are extracted using the GLCM algorithm. Then, using a variety of ML techniques, like as CNN, ANN, SVM, Gaussian NB, and Adaptive Boosting, the photos are categorized. The Gaussian NB method performs more effectively with regard to the identification and classification of brain tumors. The plasterwork work achieved 98.80 percent accuracy using GNB, RBF SVM

An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin

Brain Tumor Segmentation by Generative Adversarial Network (GAN)

The concept of a brain tumor is one of the most significant health issues in terms of both economic and social stability. This disease is extensive growth of abnormal cells in the brain and any growth inside can lead to any serious problem. The cost of a patient’s life is a primary concern, so multiple monitoring and treatment systems are still improving to build up the long-term life expectancy of the better life of those patients who have severe brain tumor problems. However, there exists a lack of data available associated with medical diagnosis and images in which intensive diagnostic analytics (DA) techniques are demanded today. In these cases, accurate performance improvement is a major factor of positive enhancement in treatment and diagnostics by the fact that a lack of medical images has constant distribution compared with real image distributions. Therefore, deep learning of structural variability of brain tumors substantially offers contrast-enhanced images to eliminate attainable data gaps and lacks in image distribution

Capsule Network-based Radiomics: From Diagnosis to Treatment

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals have resulted in a surge of significant interest in ``radiomics". Radiomics is an emerging and relatively new research field, which refers to semi-quantitative and/or quantitative features extracted from medical images with the goal of developing predictive and/or prognostic models. Radiomics is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional radiomics workflow is, typically, based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Clinical application of hand-crafted radiomics is, however, limited by the fact that features are pre-defined and extracted without taking the desired outcome into account. The aforementioned drawback has motivated trends towards development of deep learning-based radiomics (also referred to as discovery radiomics). Discovery radiomics has the advantage of learning the desired features on its own in an end-to-end fashion. Discovery radiomics has several applications in disease prediction/ diagnosis. Through this Ph.D. thesis, we develop deep learning-based architectures to address the following critical challenges identified within the radiomics domain. First, we cover the tumor type classification problem, which is of high importance for treatment selection. We address this problem, by designing a Capsule network-based architecture that has several advantages over existing solutions such as eliminating the need for access to a huge amount of training data, and its capability to learn input transformations on its own. We apply different modifications to the Capsule network architecture to make it more suitable for radiomics. At one hand, we equip the proposed architecture with access to the tumor boundary box, and on the other hand, a multi-scale Capsule network architecture is designed. Furthermore, capitalizing on the advantages of ensemble learning paradigms, we design a boosting and also a mixture of experts capsule network. A Bayesian capsule network is also developed to capture the uncertainty of the tumor classification. Beside knowing the tumor type (through classification), predicting the patient's response to treatment plays an important role in treatment design. Predicting patient's response, including survival and tumor recurrence, is another goal of this thesis, which we address by designing a deep learning-based model that takes not only the medical images, but also different clinical factors (such as age and gender) as inputs. Finally, COVID-19 diagnosis, another challenging and crucial problem within the radiomics domain, is dealt with using both X-ray and Computed Tomography (CT) images (in particular low-dose ones), where two in-house datasets are collected for the latter and different capsule network-based models are developed for COVID-19 diagnosis

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Radiological and surgical aspects of polymorphous low-grade neuroepithelial tumor of the young (PLNTY)

Background: Polymorphous low-grade neuroepithelial tumor of the young (PLNTY) is a low-grade epilepsy-associated tumor recently introduced in WHO 2021 classification. Since it has been recognized as an independent nosological entity, PLNTY has been mainly studied from a genetic and molecular perspective, not recognizing unique characteristic clinical and radiological features. Methods: A systematic literature research has been conducted aiming to identify all relevant studies about the radiological, clinical and surgical features of PLNTY. We described a representative case of a 45-year-old man treated with awake-surgery with confirmed diagnosis of PLNTY, reporting the radiological and surgical characteristics through imaging and intra-operative video. We performed a statistical meta-analysis attempting to assess the presence of relationships between surgical and radiologic tumor characteristics and clinical outcome and type of surgery. Results: A total of 16 studies were included in the systematic review. The final cohort was composed of 51 patients. Extent of resection (EOR) and outcome are not significantly associated with the different genetic profiling (p = 1), the presence of cystic intralesional component, calcification (p = 0.85), contrast-enhancing and lesion boundaries (p = 0.82). No significant correlation there is between EOR and remission or better control of epilepsy-related symptoms (p = 0.38). The contrast enhancement in the tumor is significantly associated with recurrence or poor control of epileptic symptoms (p = 0.07). Conclusions: In PLNTYs, contrast enhancement seems to impact prognosis, recurrence, and seizure control much more than radiological features, genetic features and type of resection of the tumor