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Brain Tumor Detection Using Convolutional Neural Network
... Table 4 compares the current study project with the previous research project (N. Ahmad and K. Dimililer, 2022) [15]. Brain MRI images from Kaggle by Navoneel Chakrabarty' used to detect brain tumors are the data set used in these two experiments. ...
A brain tumor is a very common and devastating malignant tumor that leads to a shorter lifespan if not detected early enough. Brain tumor classification is a critical step after the tumor has been identified to create an effective treatment plan. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are non-tumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. These accuracies positively impact the early detection of tumors before the tumor causes physical side effects such as paralysis and other disabilities.
A brain tumor is defined as any group of atypical cells occupying space in the brain. There are more than 120 types of them. MRI scans are used for brain tumor diagnosis since they are more detailed and three-dimensional. Accurate localization and segmentation of the tumor portion increase the patients' survival rates. To this end, we presented a systematic review of the latest development of brain tumor segmentation from MRI.
To find related articles, we searched the keywords like "brain tumors" and "segmentation by MRI”. The searches were performed on Elsevier, Springer, Wiley, and the leading conferences in the field of medical image processing. A total of 79 publications dedicated to tumor segmentation from years 2019 to 2023 were selected and categorized into four categories: non-Artificial Intelligence, machine learning, deep learning, and hybrid deep learning methods.
We reviewed the trending techniques of tumor segmentation and provided a unified and integrated overview of the current state-of-the-art. The article dealt with providing the capabilities and shortcomings associated with each approach and the restrictions on using automated medical image segmentation techniques in clinical practice were determined.
In this study, the advancement of brain tumor segmentation by MRI is discussed, focusing more on recent articles. It identified the restrictions of the presented techniques regarding the four mentioned categories, which prevent them from being used in clinical practice. The literature will guide the researchers to become familiar with both the leading techniques and the potential problems that need to be addressed.
In the contemporary landscape, machine learning has a pervasive impact across virtually all industries. However, the success of these systems hinges on the accessibility of training data. In today's world, every device generates data, which can serve as the building blocks for future technologies. Conventional machine learning methods rely on centralized data for training, but the availability of sufficient and valid data is often hindered by privacy concerns. Data privacy is the main concern while developing a healthcare system. One of the technique which allow decentralized learning is Federated Learning. Researchers have been actively applying this approach in various domains and have received a positive response. This paper underscores the significance of employing Federated Learning in the healthcare sector, emphasizing the wealth of data present in hospitals and electronic health records that could be used to train medical systems.
Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.
Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.
In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.
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Since human body's standard control metabolism stops, old cells do not die and these abnormal cells forms a mass of tissue, known as tumor. In order to diagnose tumors, huge biomedical machines such as MRI, PET-CT, and MG machines are used. Last decade, there are many studies in brain tumor detection in magnetic resonance imaging (MRI). Location of the brain tumor and precise size are detected with brain tumor detection. In this paper, image processing techniques are applied on MRI images to preserve information details. These techniques are erosion, contrast enhancement and median filtering. The purpose of observe is develop an image processing algorithm for brain cancer detection on MRI images. Comparison of back propagation neural networks will be done using original images and reconstructed images on the effect of categorization.
This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning problems. It deals with two significant issues in the pre-processing process (i). issues with data and (ii). Steps to follow to do data analysis with its best approach. As raw data are vulnerable to noise, corruption, missing, and inconsistent data, it is necessary to perform pre-processing steps, which is done using classification, clustering, and association and many other pre-processing techniques available. Poor data can primarily affect the accuracy and lead to false prediction, so it is necessary to improve the dataset's quality. So, data pre-processing is the best way to deal with such problems. It makes the knowledge extraction from the data set much easier with cleaning, Integration, transformation, and reduction methods. The issue with Data missing and significant differences in the variety of data always exists as the information is collected through multiple sources and from a real-world application. So, the data augmentation approach generates data for machine learning models. To decrease the dependency on training data and to improve the performance of the machine learning model. This paper discusses flipping, rotating with slight degrees and others to augment the image data and shows how to perform data augmentation methods without distorting the original data.
Research on biomedical science has many components like biomedical engineering, biomedical signal processing, gene analysis, and biomedical image processing. Classification, detection, and recognition have a great value for disease diagnosis and analysis. In this work, biomedical image classification is discussed. In one part, the brain tumor is considered with brain magnetic resonance images and in the other part, COVID affected chest X-rays have been classified using the ensemble approach. The images have been collected from Kaggle online platform. For this purpose, four heterogeneous base classifiers as Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, and Gated Recurrent Unit are considered, and metadata is generated. Further, for the detection purpose, a fuzzy min–max model is utilized to avoid uncertainty. The ensemble output from the base classifiers is fed to the fuzzy model in terms of class probability and labels. The min–max algorithm for correct decisions is used in the fuzzy model. The measuring parameters like precision, recall, accuracy, sensitivity, specificity, and F1-score are evaluated. 100% training accuracy for both the datasets is obtained whereas 97.62% and 95.24% of validation accuracy are found for brain image and chest X-ray image classification respectively as exhibited in the result section.
Brain tumor is one of the most serious scenarios associated with the brain where a cluster of abnormal cells grows in an uncontrolled fashion. The field of image processing has experienced remarkable growth in the area of biomedical applications with the invention of different techniques in deep learning. Brain tumor classification and detection is a subject of prime importance where Convolutional Neural Networks (CNN) find application. But the main drawback of the existing technology is that it is complex with a huge number of parameters contributing to high execution time and high system specifications for implementation. In this paper, a novel architecture for Brain tumor classification and tumor type object detection using the RCNN technique is proposed which has been analyzed using two publicly available datasets from Figshare (Cheng et al., Jun 2017) and Kaggle (Kaggle, June 2020). Here we aim to decrease the execution time of a conventional RCNN architecture with the use of a low complex framework and propose a system for brain tumor analysis. We first use a Two Channel CNN, a low complex architecture to classify between Glioma and healthy tumor MRI samples which was successfully done with an accuracy of 98.21 percentage. Later this same architecture is used as the feature extractor of an RCNN to detect the tumor regions of the Glioma MRI sample that has been classified from the previous stage and the tumor region is bounded using bounding boxes. Also, this method has been extended to other two types of tumors Meningioma and Pituitary tumor. The methodology was able to achieve very low execution time as compared with the other existing architectures with an average confidence level of 98.83 percentage.
Objectives
Glioma grading using maching learning on magnetic resonance data is a growing topic. According to the World Health Organization (WHO), the classification of glioma discriminates between low grade gliomas (LGG), grades I, II; and high grade gliomas (HGG), grades III, IV, leading to major issues in oncology for therapeutic management of patients. A well-known dataset for machine-based grade prediction is the MICCAI Brain Tumor Segmentation (BraTS) dataset. However this dataset is not divided into WHO-defined LGG and HGG, since it combines grades I, II and III as “lower grades gliomas”, while its HGG category only presents grade IV glioblastoma multiform. In this paper we want to train a binary grade classifier and investigate the consistency of the original BraTS labels with radiologic criteria using machine-aided predictions.
Material and methods
Using WHO-based radiomic features, we trained a SVM classifier on the BraTS dataset, and used the prediction score histogram to investigate the behaviour of our classifier on the lower grade population. We also asked 5 expert radiologists to annotate BraTS images between low (as opposed to lower) grade and high grade glioma classes, resulting in a new groundtruth.
Results
Our first training reached 84.1% accuracy. The prediction score histogram allows us to identify the radiologically high grade patients among the original lower grade population of the BraTS dataset. Training another SVM on our new radiologically WHO-aligned groundtruth shows robust performances despite important class imbalance, reaching 82.4% accuracy.
Conclusion
Our results highlight the coherence of radiologic criteria for low grade versus high grade classification under WHO terms. We also show how the histogram of prediction scores and crossed prediction scores can be used as tools for data exploration and performance evaluation. Therefore, we propose to use our radiological groundtruth for future development on binary glioma grading.
Brain tumors identification and classification is a most crucial role in medical diagnosis and treatment plan for the patients. The information acquired by medical imaging machines contains low level information which cannot be perceived my human visual system. An automatic Computer aided diagnosis system with deep convolutional networks can perform well in Medical Image classification, but requires large datasets. Generally it is difficult to obtain a large number of labelled samples in medical image classification tasks. Knowledge transfer with fine tuning of pre-trained networks can apply on small medical datasets for diagnosis. In this work transfer learning and block wise fine tuning with separate KNN classifier on pre trained deep neural network VGG net is used to classify BRATS and CE-MRI datasets. The proposed method is denoted as BT-VGGNet. The labelled BRATS Flair Images and CE-MRI T1-weighted contrast-enhanced datasets are exploited to block wise fine-tune all hidden layers in the VGG deep neural network in feature extraction and KNN for classification. Experimental results show that the proposed model exceeds the state-of-the-art classification with 97.28 percent and 98.69 percent accuracy on the BTDS-2 and CE-MRI datasets respectively. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.
Cancer detection and research on early detection solutions play life sustaining role for human health. Computed Tomography images are widely used in radiotherapy planning. Computed Tomography images provide electronic densities of tissues of interest, which are mandatory. For certain target delineation, the good spatial resolution and soft/hard tissues contrast are needed. Also, Computed Tomography techniques are preferred compared to X-Ray and magnetic resonance imaging images. Image processing techniques have started to become popular in use of Computed Tomography images. Artificial neural networks propose a quite different approach to problem solving and known as the sixth generation of computing. In this study, two phases are proposed. For first phase, image pre-processing, image erosion, median filtering, thresholding and feature extraction of image processing techniques are applied on Computed Tomography images in detail. In second phase, an intelligent image processing system using back propagation neural networks is applied to detect lung tumors.
In this work we investigate the effect of the convolutional network depth on
its accuracy in the large-scale image recognition setting. Our main
contribution is a thorough evaluation of networks of increasing depth, which
shows that a significant improvement on the prior-art configurations can be
achieved by pushing the depth to 16-19 weight layers. These findings were the
basis of our ImageNet Challenge 2014 submission, where our team secured the
first and the second places in the localisation and classification tracks
respectively.
Brain tumors are rare in infants who are younger than six months of age. These tumors can be challenging to treat surgically. We analyzed a modern series of patients treated by a multidisciplinary team at a tertiary care center and performed a literature review of this unique population.
Retrospective clinical data were collected for patients surgically treated for intracranial mass lesions at The Children's Hospital of Philadelphia from 1998 to 2007. Dermoid cysts and other skull-based lesions were excluded from the analysis.
Sixteen patients younger than six months of age underwent surgery for primary intracranial mass lesions. The median age of the patients at surgery was 5.2 months (range, 1.4-6 months of age). Children most often presented with a bulging fontanelle, hydrocephalus, or macrocephaly (seven patients). Vomiting was seen in five patients, cranial nerve palsies in one patient, and seizures in three patients. All patients had tumor resections and postoperatively were monitored in the intensive care unit. The final pathology consisted of atypical teratoid/rhabdoid tumor (three patients), primitive neuroectodermal tumor/medulloblastoma (three patients), choroid plexus papilloma (two patients), astrocytoma (two patients), ganglioglioma (two patients), desmoplastic infantile ganglioglioma (two patients), glioblastoma multiforme (one patient), and choroid plexus carcinoma (one patient). Two intraoperative deaths occurred. Of the surviving 14, a gross total resection was achieved in four. Adjuvant therapy was determined by a multidisciplinary team composed of neuro-oncology, neurosurgery, and radiation oncology. Seven patients were treated with chemotherapy, and one patient had proton beam therapy. Five-year overall survival was 45%. The eight surviving patients had neurological sequelae, and developmental outcome was variable.
Brain tumors are uncommon in children younger than six months of age. Patients present with a variety of tumor pathologies. Children who survive have neurological sequelae. More studies are necessary to understand the impact that different treatment options, tumor pathology, and tumor location have on neurological outcome.
Brain tumours in adults are a rare disease from which survival is generally poor compared to many other cancers. Reports of rising trends need to be cautiously interpreted as they may well be explained by changes in diagnostic and clinical practice. In childhood a different profile of tumour types is present and survival has improved over recent years and is higher than in adults. Apart from genetic predisposition, the most well established environmental risk factor for brain tumours is exposure to high doses of ionising radiation. Research into infections and immune factors may prove a fruitful avenue of investigation.
Rubin's pathology: clinico-pathologic foundations of medicine
- R Rubin
- D S Strayer
- E Rubin
Rubin's pathology: clinico-pathologic foundations of medicine
- rubin