Biomedical Image Classification Research Articles

Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0.

Training supervised machine learning models like deep learning requires high-quality labelled datasets that contain enough samples from various categories and specific cases. The Data as a Service (DaaS) can provide this high-quality data for training efficient machine learning models. However, the issue of privacy can minimize the participation of the data owners in DaaS provision. In this paper, a blockchain-based decentralized federated learning framework for secure, scalable, and privacy-preserving computational intelligence, called Decentralized Computational Intelligence as a Service (DCIaaS), is proposed. The proposed framework is able to improve data quality, computational intelligence quality, data equality, and computational intelligence equality for complex machine learning tasks. The proposed framework uses the blockchain network for secure decentralized transfer and sharing of data and machine learning models on the cloud. As a case study for multimedia applications, the performance of DCIaaS framework for biomedical image classification and hazardous litter management is analysed. Experimental results show an increase in the accuracy of the models trained using the proposed framework compared to decentralized training. The proposed framework addresses the issue of privacy-preserving in DaaS using the distributed ledger technology and acts as a platform for crowdsourcing the training process of machine learning models.

An enforced block diagonal low-rank representation method for the classification of medical image patterns

Low-rank representation based methods have been used on a variety of medical imaging databases for the segmentation and classification of biomedical images. The subspace segmentation of the data is performed by generating the block diagonal coefficient matrix. Whereas, the data is classified by performing the partitioning of the low-rank representation matrix. There exist several such methods for analysing medical images. The major difference between them lies in the construction of the data dictionary. Most of the time, the input data pattern is used as the dictionary for learning the representation matrix. The direct use of the input data for learning the representation degrades the performance of the model because medical images are subjected to outliers of multiple types, which include environmental lighting, image appearance and varying illumination. These types of errors induce noise in the data. It has been observed that the representation-based model is robust when the training data is clean. If the training data contains corrupted subsamples, the performance of the model drops down. We have addressed the mentioned problem by adopting a class-wise dictionary learning approach. In which the pattern of each class is learnt as the set of tuples in the dictionary. The model has been evaluated on several medical imaging datasets, which includes the Break-his dataset, ALL-IDB, biomedical images, covid CT and chest X-ray. The classification performance of the model is best for the biomedical database (99.16%) followed by the Covid dataset (94%), ALL-IDB database (93.47%) and Break-his dataset (93%).

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model on Internet of Things (IoT) environment. The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition. In addition, the pre-processing of pap smear images takes place using adaptive weighted mean filtering (AWMF) technique. Moreover, sailfish optimizer with Tsallis entropy (SFO-TE) approach has been implemented for the segmentation of pap smear images. Furthermore, a deep learning based Residual Network (ResNet50) method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images. In order to showcase the improved diagnostic outcome of the BPSIC-CDF technique, a comprehensive set of simulations take place on Herlev database. The experimental results highlighted the betterment of the BPSIC-CDF technique over the recent state of art techniques interms of different performance measures.

Spatial Feature Fusion for Biomedical Image Classification based on Ensemble Deep CNN and Transfer Learning

Spatial Feature Fusion for Biomedical Image Classification based on Ensemble Deep CNN and Transfer Learning

Design of deep ensemble classifier with fuzzy decision method for biomedical image classification

Research on biomedical science has many components like biomedical engineering, biomedical signal processing, gene analysis, and biomedical image processing. Classification, detection, and recognition have a great value for disease diagnosis and analysis. In this work, biomedical image classification is discussed. In one part, the brain tumor is considered with brain magnetic resonance images and in the other part, COVID affected chest X-rays have been classified using the ensemble approach. The images have been collected from Kaggle online platform. For this purpose, four heterogeneous base classifiers as Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, and Gated Recurrent Unit are considered, and metadata is generated. Further, for the detection purpose, a fuzzy min–max model is utilized to avoid uncertainty. The ensemble output from the base classifiers is fed to the fuzzy model in terms of class probability and labels. The min–max algorithm for correct decisions is used in the fuzzy model. The measuring parameters like precision, recall, accuracy, sensitivity, specificity, and F1-score are evaluated. 100% training accuracy for both the datasets is obtained whereas 97.62% and 95.24% of validation accuracy are found for brain image and chest X-ray image classification respectively as exhibited in the result section.

A Study on Design and Development of Framework for Content-based Image Retrieval

CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.

Biomedical Image Classification using CNN by Exploiting Deep Domain Transfer Learning

Biomedical Image Classification using CNN by Exploiting Deep Domain Transfer Learning

C-Net: A reliable convolutional neural network for biomedical image classification

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics.

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

Optimal deep learning based fusion model for biomedical image classification

AbstractAutomated examination of biomedical signals plays a vital role to diagnose diseases and offers useful data to several applications in the areas of physiology, sports medicine, and human–computer interface. The latest advancements in Artificial Intelligence (AI) have the ability to manage and analyse enormous biomedical datasets resulting in clinical decision making and real time applications. At the same time, Colorectal cancer (CRC) is the third most deadly disease affecting people over the globe. The utilization of AI techniques for the earlier identification of CRC has gained significant interest among the research communities. Therefore, this paper presents a novel AI based fusion model for CRC disease diagnosis and classification, named AIFM‐CRC. The presented AIFM‐CRC model primarily undergoes Gaussian filtering based noise removal and contrast enhancement as a preprocessing stage. In addition, a fusion based feature extraction process takes place where the SIFT based handcrafted features and Inception v4 based deep features are fused together. Besides, whale optimization algorithm tuned deep support vector machine model is employed as a classification technique to determine the existence of CRC. In order to highlight the proficient results analysis of the AIFM‐CRC model, a comprehensive simulation analysis takes place. The resultant experimental values pointed out the betterment of the AIFM‐CRC model by accomplishing a maximum accuracy of 96.18%.

A Critical Analysis of Biomedical Image Classification on Deep Learning

In computer-aided diagnostic technologies, deep convolutional neural image compression classifications are a crucial method. Conventional methods rely primarily on form, colouring, or feature descriptors, and also their configurations, the majority of which would be problem-specific that has been depicted to be supplementary in image data, resulting in a framework that cannot symbolize high problem entities and has poor prototype generalization capability. Emerging Deep Learning (DL) techniques have made it possible to build an end-to-end model, which could potentially general the last detection framework from the raw clinical image dataset. DL methods, on the other hand, suffer from the high computing constraints and costs in analytical modelling and streams owing to the increased mode of accuracy of clinical images and minimal sizes of data. To effectively mitigate these concerns, we provide a techniques and paradigm for DL that blends high-level characteristics generated from a deep network with some classical features in this research. The following stages are involved in constructing the suggested model: Firstly, we supervisedly train a DL model as a coding system, and as a consequence, it could convert raw pixels of medical images into feature extraction, which possibly reflect high-level ideologies for image categorization. Secondly, using image data background information, we derive a collection of conventional characteristics. Lastly, to combine the multiple feature groups produced during the first and second phases, we develop an appropriate method based on deep neural networks. Reference medical imaging datasets are used to assess the suggested method. We get total categorization reliability of 90.1 percent and 90.2 percent, which is greater than existing effective approaches.

Democratizing AI in biomedical image classification using virtual reality

Virtual reality (VR) offers immersive visualization and intuitive interaction. We leverage VR to enable any biomedical professional to deploy a deep learning (DL) model for image classification. While DL models can be powerful tools for data analysis, they are also challenging to understand and develop. To make deep learning more accessible and intuitive, we have built a virtual reality-based DL development environment. Within our environment, the user can move tangible objects to construct a neural network only using their hands. Our software automatically translates these configurations into a trainable model and then reports its resulting accuracy on a test dataset in real-time. Furthermore, we have enriched the virtual objects with visualizations of the model's components such that users can achieve insight about the DL models that they are developing. With this approach, we bridge the gap between professionals in different fields of expertise while offering a novel perspective for model analysis and data interaction. We further suggest that techniques of development and visualization in deep learning can benefit by integrating virtual reality.

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow.

Tens of images can suffice to train neural networks for malignant leukocyte detection

Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analyze the binary classification performance of a CNN on two independent cytomorphology datasets as a function of training set size. Specifically, we train a sequential model to discriminate non-malignant leukocytes from blast cells, whose appearance in the peripheral blood is a hallmark of leukemia. We systematically vary training set size, finding that tens of training images suffice for a binary classification with an ROC-AUC over 90%. Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased. A low dimensional tSNE representation reveals that while the two classes are separated already for a few training images, the distinction between the classes becomes clearer when more training images are used. To evaluate the performance in a multi-class problem, we annotated single-cell images from a acute lymphoblastic leukemia dataset into six different hematopoietic classes. Multi-class prediction suggests that also here few single-cell images suffice if differences between morphological classes are large enough. The incorporation of deep learning algorithms into clinical practice has the potential to reduce variability and cost, democratize usage of expertise, and allow for early detection of disease onset and relapse. Our approach evaluates the performance of a deep learning based cytology classifier with respect to size and complexity of the training data and the classification task.

A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier

Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10.

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images.

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework.

Biomedical image classification using deep convolutional neural networks – overview

Deep Learning is an advanced area of machine learning which gained much interest in the past decades. It has been widely used in a variety of applications and has proved to be an effective machine learning method for many complicated issues. Especially when it comes to the medical field, the classification of biomedical images is a complex task to identify and classify the images manually by the doctors. So, Deep Learning is a key to enhance the classification of biomedical images using various architectures. The biomedical picture classification aims to identify and classify biomedical characteristics efficiently, which have significant advantages to numerous study and development fields. In this paper, the framework focused on the different architectures that were used to classify the medical images along with their performances.

Thyroid nodules segmentation methods in clinical ultrasound images: A review

Thyroid is an extremely essential butterfly shaped organ which is positioned in front of neck. For finding thyroid disease, ultrasound imaging is considered a best for identification of thyroid nodules because of its inexpensive and trouble-free nature. For diagnosis of thyroid disorder, it is very important to separate thyroid nodules from thyroid gland. For the same in biomedical image processing, image segmentation and classification followed by image preprocessing is very imperative steps. The key goal of this work is to categorized of thyroid nodules, thyroid gland segmentation methods. This paper explained different segmentation methods like counter and shaped based, region based and machine learning and deep learning strategies for thyroid gland and thyroid nodules. In addition, grouping of thyroid knobs is classified by traditional and convolution neural network for different feature extraction methods. All these methods are evaluated and compared by different quality metrics. After careful assessment of plenty of paper we did comparative and comprehensive analysis of segmentation methods with deep learning. Segmentation by deep learning approach gives better performance but it required more marked data base. In thyroid nodules segmentation region-based methods and contour and shape-based methods gives better performance. This paper concludes segmentation methods and their performance which is based on image attributes applications etc. The performance is assets by accuracy, selectivity and specificity and mean overlap parameters. Moreover, we concentrate on several advantages and disadvantages of current thyroid nodules ultrasound image segmentation of thyroid nodules and thyroid gland methods for 2Dimentionsl Ultrasound image, which would be useful for future studies such as identification of specific thyroid disease.

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field [...]

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs