U-Net Analysis Architecture For MRI Brain Tumor Segmentation

Identification, segmentation and detection of brain tumor-infected parts on MRI images require precision and a long time. MRI of the brain has an important role, one of which is used for analysis or consideration before performing surgery. However, MRI images cannot provide optimal results when analyzed because of the presence of noise and the bone and tumor (clots of flesh) have the same appearance. Many studies related to brain tumor segmentation have been carried out before, and some of the good methods are CNN U-Net. We segmented brain tumors on MRI with U-Net. The purpose of this study was to analyze the results of changes in the number of neurons in the convolution layer of the U-Net architecture in segmenting brain tumors. We use two scenarios of changing the number of neurons at the U-Net convolution layer. The first scenario is the number of neurons successively at each level of the U-Net architecture [32,64,128,256,512], and the second scenario is [16,32,64,128,256]. And the results of scenario two can segment brain tumors on MRI images that resemble ground truth. The results of brain tumor segmentation in MRI images with the U-Net second scenarios have an average Dice value of 0.768

Deep learning based Brain Tumour Classification based on Recursive Sigmoid Neural Network based on Multi-Scale Neural Segmentation

Brain tumours are malignant tissues in which cells replicate rapidly and indefinitely, and tumours grow out of control. Deep learning has the potential to overcome challenges associated with brain tumour diagnosis and intervention. It is well known that segmentation methods can be used to remove abnormal tumour areas in the brain. It is one of the advanced technology classification and detection tools. Can effectively achieve early diagnosis of the disease or brain tumours through reliable and advanced neural network classification algorithms. Previous algorithm has some drawbacks, an automatic and reliable method for segmentation is needed. However, the large spatial and structural heterogeneity between brain tumors makes automated segmentation a challenging problem. Image tumors have irregular shapes and are spatially located in any part of the brain, making their segmentation is inaccurate for clinical purposes a challenging task. In this work, propose a method Recursive SigmoidNeural Network based on Multi-scale Neural Segmentation (RSN2-MSNS) for image proper segmentation. Initially collets the image dataset from standard repository for brain tumour classification.  Next, pre-processing method that targets only a small part of an image rather than the entire image. This approach reduces computational time and overcomes the over complication. Second stage, segmenting the images based on the Enhanced Deep Clustering U-net (EDCU-net) for estimating the boundary points in the brain tumour images. This method can successfully colour histogram values are evaluating segment complex images that contain both textured and non-textured regions. Third stage, Feature extraction for extracts the features from segmenting images using Convolution Deep Feature Spectral Similarity (CDFS2) scaled the values from images extracting the relevant weights based on its threshold limits. Then selecting the features from extracting stage, this selection is based on the relational weights. And finally classified the features based on the Recursive Sigmoid Neural Network based on Multi-scale Neural Segmentation (RSN2-MSNS) for evaluating the proposed brain tumour classification model consists of 1500 trainable images and the proposed method achieves 97.0% accuracy. The sensitivity, specificity, detection accuracy and F1 measures were 96.4%, 952%, and 95.9%, respectively

Beyin Tümör Tespiti İçin Derin Öğrenme Temelli Bilgisayar Destekli Tanı Sistemi

Beyin MR segmentasyonu klinik uygulamalarda önem arz etmektedir. Beyin analizi çeşitli yaklaşımlarla bulgular ve anatomik bölgelerin doğru segmentasyonuna dayanır. Beyin MRI analizi, epilepsi, şizofreni, alzheimer, kanser ve bulaşıcı dejeneratif hastalıklar gibi beyin bozukluklarının tedavisi için yaygın bir şekilde kullanılmaktadır. Hasta MRI görüntülerinin doktorlar tarafından manuel segmentasyonu görüntülerin dilim dilim ana hatlarının çıkarılmasını gerektirir. Ancak manuel segmentasyonun uzman görüşü ve teknolojik kısıtları nedeniyle bazı dezavantajları vardır. Bununla birlikte görüntü yorumlama son derece zaman alan bir işlemdir. Tecrübeye bağlı olarak hata yapma oranı da yüksektir. Bu çalışmada, beyin MR görüntülerinden otomatik tümör tespiti için uçtan uca Çok Ölçekli Çok Düzeyli Ağ (Multi-Scale Multi-Level Network MM-Network) modeli önerilmiştir. Gerçekleştirilen çalışmada, UNet'teki evrişimli ağ seviyesinde çoklu uzamsal ölçeklerin küresel bağlamsal özelliklerini birleştirerek, ağlar boyunca özellik haritalarının boyutuna bağlı olarak alıcı alanın farklı oranlarda genişlemesini sağlayan genişletilmiş evrişim modülünden yararlanılmıştır. Yapılan deneysel çalışmalarda önerilen model ile yüksek doğrulukta tümör tespiti sağlanmıştır

High-Resolution Semantic Segmentation of Woodland Fires Using Residual Attention UNet and Time Series of Sentinel-2

Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) > 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models-especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.Peer reviewe

Detection of Detached Ice-fragments at Martian Polar Scarps Using a Convolutional Neural Network

Repeated high-resolution imaging has revealed current mass wasting in the form of ice block falls at steep scarps of Mars. However, both the accuracy and efficiency of ice-fragments’ detection are limited when using conventional computer vision methods. Existing deep learning methods suffer from the problem of shadow interference and indistinguishability between classes. To address these issues, we proposed a deep learning-driven change detection model that focuses on regions of interest. A convolutional neural network simultaneously analyzed bitemporal images, i.e., pre- and postdetach images. An augmented attention module was integrated in order to suppress irrelevant regions such as shadows while highlighting the detached ice-fragments. A combination of dice loss and focal loss was introduced to deal with the issue of imbalanced classes and hard, misclassified samples. Our method showed a true positive rate of 84.2% and a false discovery rate of 16.9%. Regarding the shape of the detections, the pixel-based evaluation showed a balanced accuracy of 85% and an F1 score of 73.2% for the detached ice-fragments. This last score reflected the difficulty in delineating the exact boundaries of some events both by a human and the machine. Compared with five state-of-the-art change detection methods, our method can achieve a higher F1 score and surpass other methods in excluding the interference of the changed shadows. Assessing the detections of the detached ice-fragments with the help of previously detected corresponding shadow changes demonstrated the capability and robustness of our proposed model. Furthermore, the good performance and quick processing speed of our developed model allow us to efficiently study large-scale areas, which is an important step in estimating the ongoing mass wasting and studying the evolution of the martian polar scarps

A Performance-Consistent and Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

Brain tumors cause serious health problems and brain tumor detection is important for the diagnosis. The detection is a very challenging task due to the complexity in brain structures and in brain tumor patterns. Manual segmentation requires an expertise of highly trained medical specialists and is very time-consuming. Therefore, it’s imperative to develop fully automated brain tumor segmentation systems, i.e., CNN based systems, to accelerate the diagnosis process. The research on developing such systems has been progressed rapidly in recent years. For the systems to be applicable in practice, a good processing quality and reliability are required. Moreover, for a wide range of applications of such systems, a minimization of computation complexity is desirable, which can also result in a minimization of randomness in computation and, consequently, a better performance consistency. In this thesis, a new CNN system for brain tumor segmentation is proposed. The CNN in the proposed system is custom-designed with 2 distinguished characters dedicated to optimizing the feature extraction and classification processes. Firstly, there are three paths in its feature extraction block, designed to extract, from the multi-modality input, comprehensive feature information of mono-modality, paired-modality and cross-modality, respectively. Also, it has a particular three-branch classification block to identify the pixels of 4 classes, namely, whole tumor, enhancing tumor, non-enhancing core/necrotic tumor and those in the background. By means of the three branches, a complex multi-class classification problem is decomposed into several simple binary classification problems. Each branch is trained separately so that the parameters are adjusted specifically to suit the detection of one specific kind of tumor areas. The parameters of the convolution layers in the proposed system are determined to suit the specific purposes so that the computation volume for each filtering operations in each layer are just-sufficient, which results in a very simple config of 61,843 parameters in total, while most existing CNN systems require multi-millions. The performance of the proposed system has been tested extensively with BraTS 2018 and BraTS 2019 data samples. A good mean Dice scores in each experiment has been obtained. The average of the mean Dice scores obtained from ten experiments are very close to each other with very small deviations. In the case of the 10 experiments on BraTS 2018 validation samples, the average Dice scores and their standard deviations are 0.787±0.003, 0.886±0.002, 0.801±0.007, respectively, for enhancing tumor, whole tumor and tumor core. For the validation samples of BraTS 2019 in 10 experiments, the average Dice scores and standard deviations of enhancing tumor, whole tumor and tumor core are 0.751±0.007, 0.885±0.002, 0.776±0.004, respectively. The test results demonstrate that the proposed system is able to perform high-quality segmentation in a consistent manner. Furthermore, it only requires 146G FLOPs to complete a segmentation of the four 3D images (155x240x240x4 voxels) of a single patient case. The extremely low computation complexity of the proposed system will facilitate its implementation/application in various environments. The high processing quality and low computation complexity of the proposed system make it implementable in various environments. It can be expected that such system will have wide applications in medical image processing