Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Recently, Owen(1993) and Owen and Ledlow (1994) have shown that the dividing line between Fanaroff-Riley class~I and class~II Radio Sources is very sharp when the sources are represented as points in the radio-optical luminosity plane. It is shown that if one accepts the propositions that the sources in the vicinity of this dividing line are characterized by (1) transonic Mach numbers and (2) mildly relativistic velocities (consistent with deceleration of an initially moderately relativistic or ultrarelativistic jet), then the slope of the dividing line is readily obtained using simple physical arguments and established empirical relationships between the X-Ray luminosities, core radii, velocity dispersions and absolute magnitudes of elliptical galaxies. The intercept of the dividing line depends upon parameters which are known perhaps to within factors of order unity and agrees with the data to within an order of magnitude. ROSAT observations of elliptical galaxies will be important in constraining the central pressures and X-Ray core radii of radio ellipticals. Knowledge of these two parameters will assist in a more detailed assessment of the physics which is proposed here as being relevant. High resolution observations of jets within a kpc of the radio core will also be useful for determining the spreading rates of jets in this region.

Relativistic Jets and the Fanaroff-Riley Classification of Radio Galaxies

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.

Deep learning for chest X-ray analysis: A survey.

An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

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Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

From the early stages of Astronomy, the classification of galaxies has been a conundrum that has left astrophysicists in a situation of quandary. Although, previous methods did a phenomenal job in classifying galaxies but while analysing them, certain inefficiencies had been revealed which cannot be overlooked. The objective had been to conduct an analysis of different types of machine learning techniques that have been used to classify galaxies. This analysis had been conducted on the basis of different attributes taken for different types of classification of galaxies. A method had been proposed to classify galaxies with higher accuracy than previous methods. The configuration for the literature analysis used datasets such as ESO-LV and SDSS and discussed the antecedent techniques for classifying galaxies. It had been inferred that a Convolutional Neural Network with certain data augmentation for irregular Galaxies (Irr) gives the best result of all the algorithms that have been discussed in the literature and its analysis. Owing to the aforementioned, an implementation accentuating the use of deep learning algorithms with certain Data Augmentation techniques and certain different activation functions, named daMCOGCNN (data augmentation-based MOrphological Classifier Galaxy Using Convolutional Neural Networks) had been proposed for morphological classification of galaxies. The dataset comprises of 4614 images from SDSS Image Gallery, Galaxy Zoo challenge and Hubble Image Gallery. The efficient implementation of this method gave a testing accuracy of approximately 98% and 97.92% accuracy had been achieved on a dataset taken from different websites such as AstroBin and other such sources. The model introduced here outperforms its earlier contemporaries.

Data augmentation based morphological classification of galaxies using deep convolutional neural network

INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis. OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (AiCNNs) that accurately classifies brain MRI scans to 3 classes of brain tumor and negative diagnosis results. METHODS: AiCNNs model integrates 5 already trained models including simple convolutional neural networks (one uses a simple CNN while the other utilizes data augmentation) and three pre-trained networks whose weights are transferred from ImageNet dataset. RESULTS: AiCNNs model was trained on 3501 augmented T1-weighted contrast enhanced MRI (CE-MRI) brain images. Validation results of 99.49% (loss=0.0303) had been achieved by AiCNNs on a set of 1167 images, which outperform its contemporaries which have got results upto 97.81% (loss=0.1794) and 97.79% (loss=0.1787). CONCLUSION: AiCNNs has been shown to obtained a test accuracy of 98.89 % on a set of 1167 images.

/pdf/aicnns-artificially-integrated-convolutional-neural-networks-2bvftygmpk.pdf

AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction

—Multi-Agent RL or MARL is one of the complex problems in Autonomous Driving literature that hampers the release of fully-autonomous vehicles today. Several simulators have been in iteration after their inception to mitigate the problem of complex scenarios with multiple agents in Autonomous Driving. One such simulator–SMARTS, discusses the importance of cooperative multi-agent learning. For this problem, we discuss two approaches–MAPPO and MADDPG, which are based on-policy and off-policy RL approaches. We compare our results with the state-of-the-art results for this challenge and discuss the potential areas of improvement while discussing the explain- ability of these approaches in conjunction with waypoints in the SMARTS environment.

On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment

Detecting suspicious activities in surveillance videos is a longstanding problem in real-time surveillance that leads to difﬁculties in detecting crimes. Hence, we propose a novel approach for detecting and summarizing suspicious activities in surveillance videos. We have also created ground truth summaries for the UCF-Crime video dataset. We mod-ify a pre-existing approach for this task by leveraging the Human-Object Interaction (HOI) model for the Visual features in the Bi-Modal Transformer. Further, we validate our approach against the existing state-of-the-art algorithms for the Dense Video Captioning task for the ActivityNet Captions dataset. We observe that this formulation for Dense Captioning performs signiﬁcantly better than other discussed BMT-based approaches for BLEU@1, BLEU@2, BLEU@3, BLEU@4, and METEOR. We further perform a comparative analysis of the dataset and the model to report the ﬁndings based on different NMS thresholds (searched using Genetic Algorithms). Here, our formulation outperforms all the models for BLEU@1, BLEU@2, BLEU@3, and most models for BLEU@4 and METEOR falling short of only ADV-INF Global by 25% and 0.5%, respectively.

Ansh Mittal

Papers

Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

Data augmentation based morphological classification of galaxies using deep convolutional neural network

AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction

On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment

SAVCHOI: Detecting Suspicious Activities using Dense Video Captioning with Human Object Interactions