A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

Gastrointestinal Tract Infections Classification Using Deep Learning

Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches.

Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.