Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and time-consuming.Also,it always requires an expert person for the diagnosis.Therefore,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature.This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm.NasNet-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI images.The haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast enhancement.Next,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling layer.Then,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better classification.Although the information was increased,computational time also jumped.This issue is resolved using a hybrid feature optimization algorithm that chooses the best classification features.The experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,respectively.The proposedmethod is comparedwith several recent studies andoutperformed inaccuracy.In addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.

Crosstalk of liver immune cells and cell death mechanisms in different murine models of liver injury and its clinical relevance

BACKGROUND： Liver inflammation or hepatitis is a result of pluripotent interactions of cell death molecules, cytokines, chemokines and the resident immune cells collectively called as microenvironment. The interplay of these inflammatory mediators and switching of immune responses during hepatotoxic, viral, drug-induced and immune cell-mediated hepatitis decide the fate of liver pathology. The present review aimed to describe the mechanisms of liver injury, its relevance to human liver pathology and insights for the future therapeutic interventions.DATA SOURCES： The data of mouse hepatic models and rele- vant human liver diseases presented in this review are system- atically collected from PubMed, ScienceDirect and the Web of Science databases published in English. RESULTS： The hepatotoxic liver injury in mice induced by the metabolites of CC14, acetaminophen or alcohol represent ne- crotic cell death with activation of cytochrome pathway, for- mation of reactive oxygen species （ROS） and mitochondrial damage. The Fas or TNF-a induced apoptotic liver injury was dependent on activation of caspases, release of cytochrome c and apoptosome formation. The ConA-hepatitis demonstrat- ed the involvement of TRAIL-dependent necrotic/necroptotic cell death with activation of RIPK1/3. The a-GalCer-induced liver injury was mediated by TNF-a. The LPS-induced hepatitis involved TNF-a, Fas/FasL, and perforin/granzyme cell death pathways. The MHV3 or Poly（I：C） induced liver injury was mediated by natural killer cells and TNF-a signaling. The necrotic ischemia-reperfusion liver injury was mediated by hypoxia, ROS, and pro-inflammatory cytokines; however, necroptotic cell death was found in partial hepatectomy. The crucial role of immune ceils and cell death mediators in viral hepatitis （HBV, HCV）, drug-induced liver injury, non-alcohol- ic fatty liver disease and alcoholic liver disease in human were discussed. CONCLUSIONS： The mouse animal models of hepatitis provide a parallel approach for the study of human liver pathology. Blocking or stimulating the pathways associated with liver cell death could unveil the novel therapeutic strategies in the management of liver diseases.

Food intake regulation by leptin:Mechanisms mediating gluconeogenesis and energy expenditure

Regulation of blood glucose levels and body fat is critical for survival.Leptin circulates freely in blood and controls body weight and food intake mainly through hypothalamic receptors and regulates glucose metabolism in the liver both directly through leptin receptors and indirectly via the hypothalamic receptors of central nervous system.Leptin affects food intake regulation and eventually glucose metabolism, lipometabolism,endocrine and immune functions, reproductive function, adipose tissue metabolism and energy expenditure.Leptin also exerts peripheral effects directly on glucose metabolism and gluconeogenesis.Most of obese human subjects have elevated plasma levels of leptin associated to the size of their total adipose tissue mass.Hence gluconeogenic function may be an essential factor in the regulation of nutritional intake and weight gain.The aim of this review is therefore to identify and module the possible effects of leptin with special application in gluconeogenesis.In addition, this review includes the study of fat consumption and energy expenditure in the body.Specific modulation of leptin receptors and adipose tissues functioning could have important inference on therapeutic strategies.

COVID-19 and comorbidities of hepatic diseases in a global perspective

The worldwide outbreak of coronavirus disease 2019(COVID-19) has challenged the priorities of healthcare system in terms of different clinical management and infection transmission, particularly those related to hepatic-disease comorbidities. Epidemiological data evidenced that COVID-19 patients with altered liver function because of hepatitis infection and cholestasis have an adverse prognosis and experience worse health outcomes. COVID-19-associated liver injury is correlated with various liver diseases following a severe acute respiratory syndrome-coronavirus type 2(SARS-CoV-2) infection that can progress during the treatment of COVID-19 patients with or without pre-existing liver disease. SARS-CoV-2 can induce liver injury in a number of ways including direct cytopathic effect of the virus on cholangiocytes/hepatocytes, immune-mediated damage, hypoxia, and sepsis. Indeed, immediate cytopathogenic effects of SARSCoV-2 via its potential target, the angiotensin-converting enzyme-2 receptor, which is highly expressed in hepatocytes and cholangiocytes, renders the liver as an extra-respiratory organ with increased susceptibility to pathological outcomes. But, underlying COVID-19-linked liver disease pathogenesis with abnormal liver function tests(LFTs) is incompletely understood. Hence, we collated COVID-19-associated liver injuries with increased LFTs at the nexus of pre-existing liver diseases and COVID-19, and defining a plausible pathophysiological triad of COVID-19, hepatocellular damage, and liver disease. This review summarizes recent findings of the exacerbating role of COVID-19 in pre-existing liver disease and vice versa as well as international guidelines of clinical care, management, and treatment recommendations for COVID-19 patients with liver disease.

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in farming.In Pakistan,several kinds of fruits are produced and exported the other countries.Citrus is an important fruit,and its production in Pakistan is higher than the other fruits.However,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this Fruit.The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure.In the agriculture sector,deep learning showing significant success in the last five years.This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification.In the proposed framework,the augmentation technique is applied initially by creating more training data from existing samples.They were then modifying the two pre-trained models named Resnet18 and Inception V3.The modified models are trained using an augmented dataset through transfer learning.Features are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few more.The experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus Fruits.On these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,respectively.The proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classicatio

Background:A brain tumor reects abnormal cell growth.Challenges:Surgery,radiation therapy,and chemotherapy are used to treat brain tumors,but these procedures are painful and costly.Magnetic resonance imaging(MRI)is a non-invasive modality for diagnosing tumors,but scans must be interpretated by an expert radiologist.Methodology:We used deep learning and improved particle swarm optimization(IPSO)to automate brain tumor classication.MRI scan contrast is enhanced by ant colony optimization(ACO);the scans are then used to further train a pretrained deep learning model,via transfer learning(TL),and to extract features from two dense layers.We fused the features of both layers into a single,more informative vector.An IPSO algorithm selected the optimal features,which were classied using a support vector machine.Results:We analyzed high-and low-grade glioma images from the BRATS 2018 dataset;the identication accuracies were 99.9%and 99.3%,respectively.Impact:The accuracy of our method is signicantly higher than existing techniques;thus,it will help radiologists to make diagnoses,by providing a“second opinion.”

Novel Approach for a van der Pol Oscillator in the Continuous Time Domain

We investigate the continuous time domain numerical treatment of a van der Pol oscillator,applying the trial solution as an artificial feed-forward neural network model containing unknown adjustable parameters.The optimization of the network is performed by simulated annealing in an unsupervised method.The proposed scheme is tested successfully by its application in both non-stiff and stiff conditions.Its reliability and effectiveness is validated through comprehensive statistical analyses.The obtained results are in good agreement with the classical RK45 method.

Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations

We present an evolutionary computational approach for the solution of nonlinear ordinary differential equations(NLODEs).The mathematical modeling is performed by a feed-forward artificial neural network that defines an unsupervised error.The training of these networks is achieved by a hybrid intelligent algorithm,a combination of global search with genetic algorithm and local search by pattern search technique.The applicability of this approach ranges from single order NLODEs,to systems of coupled differential equations.We illustrate the method by solving a variety of model problems and present comparisons with solutions obtained by exact methods and classical numerical methods.The solution is provided on a continuous finite time interval unlike the other numerical techniques with comparable accuracy.With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.