A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.