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.

Design of super-orthogonal space-time trellis codes based on trace criterion

A design of super-orthogonal space-time trellis codes （SOSTTCs） based on the trace criterion （TC） is proposed for improving the design of SOSTTCs. The shortcomings of the rank and determinant criteria based design and the advantages of the TC-based design are analyzed. The optimization principle of four factors is presented, which includes the space-time block coding （STBC） scheme, set partitioning, trellis structure, and the assignment of signal subsets and STBC schemes in the trellis. According to this principle, systematical and handcrafted design steps are given in detail. By constellation expansion, the code performance can be further improved. The code design results are given, and the new codes outperform others in the simulation.

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.

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.

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.

Nano-Microplate Gold Clay for Handcraft Jewelry and Decoration

Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and burning,the organic binder and water were decomposed and the gold particles were transformed to its final metal state.Although,gold clay is very expensive,it is useful to decorate the silver clay designed jewelry or small sculptures.In this research,nano-microplate gold and specific organic binder was used for producing nano-microplate gold clay.The objectives of this research are to study binder's type and ratios for optimum producing gold clay,and to study the heating condition for making silver and gold clay jewelry.The result showed that the clay can be fired with heating temperature at 900°C for an hour by electric kiln.The physical properties of the gold clay at different heating temperatures were determined.Furthermore,prototype of jewelry using the clay was

Design of a(480,240)CMOS Analog Low-Density Parity-Check Decoder

Digital low-density parity-check(LDPC) decoders can hardly meet the power-limits brought by the new application scenarios. The analog LDPC decoder, which is an application of the analog computation technology, is considered to have the potential to address this issue to some extent. However, due to the lack of automation tools and analog stopping criteria, the analog LDPC decoders suffer from costly handcraft design and additional decoding delay, and are not feasible to practical applications. To address these issues, a decoder architecture using reusable building blocks is designed to lower the handcraft design, and a probability stopping criterion that is specially designed for analog decoder is further planned and implemented to reduce the decoding delay. Then, a(480,240) CMOS analog LDPC decoder is designed and fabricated in a 0.35-μm CMOS technology. Experimental results show that the decoder prototype can achieve 50 Mbps throughput when the power consumption is about 86.3m W, and the decoding delay can be reduced by at most 93% compared with using the preset maximum decoding delay in existing works.

A Review on Deep Learning in Medical Image Reconstruction

Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases.Medical image reconstruction is one of the most fundamental and important components of medical imaging,whose major objective is to acquire high-quality medical images for clinical usage at the minimal cost and risk to the patients.Mathematical models in medical image reconstruction or,more generally,image restoration in computer vision have been playing a prominent role.Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed,and we shall call these models handcrafted models.Later,handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs,while part of the model is learned from the observed data.More recently,as more data and computation resources are made available,deep learning based models(or deep models)pushed the data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs.Both handcrafted and data-driven modeling have their own advantages and disadvantages.Typical handcrafted models are well interpretable with solid theoretical supports on the robustness,recoverability,complexity,etc.,whereas they may not be flexible and sophisticated enough to fully leverage large data sets.Data-driven models,especially deep models,on the other hand,are generally much more flexible and effective in extracting useful information from large data sets,while they are currently still in lack of theoretical foundations.Therefore,one of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches.The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint.This viewpoint stimulates new designs of neural network architectures with inspirations from optimization algorithms and numerical differential equations.Given the popularity of deep modeling,there are still vast remaining challenges in the field,as well as opportunities which we shall discuss at the end of this article.