Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley’s wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard’s coefficient, spatial overlap, AVME, and FoM.

A Novel 2-Phase U-net Algorithm Combined with Optimal Mass Transportation for 3D Brain Tumor Detection and Segmentation

Utilizing the optimal mass transportation (OMT) technique to convert an irregular 3D brain image into a cube, a required input format for the U-net algorithm, is a brand new idea for medical imaging research. We develop a cubic volume-measure-preserving OMT (V-OMT) model for the implementation of this conversion. The contrast-enhanced histogram equalization grayscale of fluid attenuated inversion recovery (FLAIR) in a brain image creates the corresponding density function. We then propose an effective two-phase U-net algorithm combined with the V-OMT algorithm for training and validation. First, we use the U-net and V-OMT algorithms to precisely predict the whole tumor (WT) region. Second, we expand this predicted WT region with dilation and create a smooth function by convoluting the step-like function associated with the WT region in the brain image with a 5×5×5 blur tensor. Then, a new V-OMT algorithm with mesh refinement is constructed to allow the U-net algorithm to effectively train Net1--Net3 models. Finally, we propose ensemble voting postprocessing to validate the final labels of brain images. We randomly choose 1000 and 251 brain samples from theBraTS 2021 training dataset, which contains 1251 samples, for training and validation, respectively. The Dice scores of the WT, tumor core (TC) and enhanced tumor (ET) regions for validation computed by Net1--Net3 were 0.93705, 0.90617 and 0.87470, respectively. A significant improvement in brain tumor detection and segmentation with higher accuracy is achieved.

An internet of things-based automatic brain tumor detection system

Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.

MRI Image Based Classification Model for Lung Tumor Detection Using Convolutional Neural Networks

Lung tumor is a dangerous disease with the most noteworthy effects and causing more deaths around the world. Medical diagnosis of lung tumor growth can essentially lessen the death rate, on the grounds that powerful treatment alternatives firmly rely upon the particular phase of disease. Medical diagnosis considers to the use of innovation in science with the end goal of analyzing the interior structure of the organs of the human body. It is an approach to improve the nature of the patient's life through a progressively exact and fast detection, and with restricted symptoms, prompting a powerful generally treatment methodology. The main goal of the proposed work is to design a Lung Tumor Detection Model using Convolution Neural Networks (LTD-CNN) with machine learning technique that spread both miniaturized scale and full scale image surfaces experienced in Magnetic Resonance Imaging (MRI) and advanced microscopy modalities separately. Image pixels can give critical data on the abnormality of tissue and performs classification for accurate tumor detection. The advancement of Computer-Aided Diagnosing (CAD) helps the doctors and radiologists to analyze the lung disease precisely from CT images in its beginning phase. Different methods are accessible for the lung disease recognition, however numerous methodologies give not so much exactness but rather more fake positives. The proposed method is compared with the traditional models and the results exhibit that the proposed model detects the tumor effectively and more accurately.