Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity

A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction

Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods

A Multimodal Deep Learning Approach for Identification of ‎Severity of Reflective Depression ‎

Social media consumes a greate time of our dialy times that generate a significant amount of information through expressing feeling and activities, sharing admiral contents, viewing, and more. This information mostly contains valuable discoveries. Despite many attempts to mining such produced data, it is still unexploited in certain issues and attracts many research areas. In this paper, we use the data extracted from social media from female’s pages to detect possibility of depression. A new deep learning model based on the psycholinguistic vocabulary to create the embedding words is developed. First, we extract the features from the data before and after the preprocessing phase. Second, the Convolutional Neural Network (CNN) is used to label the data for extracting the remaining features. Based on the previouse two phases; the developed model succeeded to predict the depression possibilty. Adetailed comparative analysis is also presented for the evaluation of the proposed system. The proposed indicator model proved promising results in predicting depression

Physicochemical Studies and Biological Activity of Mixed Ligand Complexes Involving Bivalent Transition Metals, 2-Aminomethylthiophenyl-4-Bromosalicylaldehyde Schiff Base and Glycine

The Mixed-ligand complexes of Co(II), Ni(II), Mn(II) and Zn(II) with 2-aminomethylthiophenyl-4-bromosalicylaldehyde Schiff base (ATS) and glycine as a representative example of amino acids have been achieved. These complexes namely [Ni(ATS)(Gly)] (1), [Co(ATS)(Gly)].H2O (3) [Mn(ATS)(Gly)].2H2O (2)  and [Zn(ATS)(Gly)] (4) were characterized by elemental, molar conductance, infrared, magnetic moment, and electronic spectra. ATS behaves as mononegatively charged bidentate ligand with coordination through azomethine nitrogen and phenolate oxygen groups while glycine acts as monobasic bidentate ligand is coordinated via amino and ionized carboxylate group after deprotonation. The low molar conductance values suggest the non-electrolytic nature of these complexes. The magnetic and spectral data indicates a square planar geometry for Ni2+ complex, tetrahedral geometry for Zn2+ and octahedral geometry for both Mn2+ and Co2+ complexes. The isolated chelates have been screened for their antimicrobial activity. Keywords: Schiff base, Nickel(II); Glycine; Electronic Spectra; antibacterial activity