Stain deconvolution using statistical analysis of multi-resolution stain colour representation

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners

Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications

Automatic analysis of lung adenocarcinoma histology whole slide images

Histology is the backbone in the diagnosis and prognosis pipeline of most types of cancer, especially lung adenocarcinoma (LUAD). However, a pathologist's assessment of histology slides is often subjective, semi-quantitative, and limited to selected regions of the tumour. Recently, automatic tools are being widely employed by utilising digital slide scanners. These tools exploit a large amount of information captured from the tumour which could not be achieved by human observers. In addition, the automatic tools enable an objective and reproducible way of conducting the clinical experiments, and provide the opportunity to discover new potential image-based features. Utilising these features could provide a second opinion for the pathologists in different tasks such as distinguishing between various types of lung cancer, predicting possible metastases and others. Therefore, a solid foundation is provided towards objective, comparable histology assessments, and personalised treatment of the disease. In this thesis, we examine different stages of LUAD automatic analysis. We begin with preprocessing of the WSI, then cell analysis, and finally tumour morphology analysis. We use scanned slides of tissue sections stained with Haematoxylin and Eosin (H&E). In the first part of the thesis, we start with preprocessing of histology images. We propose two methods for stain deconvolution: First, a supervised method where a classifier is trained to distinguish between different stain colours. We transfer the stain colours into another chromatic colour space such that the intensity variations between pixels of one stain colour are minimised. Second, we present an unsupervised method which directly estimates the separation between different stain colours by filtering the image such that the dependency between the stain colours is reduced. In the second part of the thesis, we propose two novel automatic tools to quantify the heterogeneity of tumour cells and tissue morphology. The first tool performs WSI analysis of cellular features by extracting statistics from the WSI heat map and then examining these statistics to find the correlation with survival of LUAD patients. In the second tool, we propose an automatic method for quantification of LUAD growth patterns in WSI using a deep-learning-based method. The proposed framework is applied to automatically locate and classify tumour growth patterns within the WSI. Finally, we quantify the percentages of each pattern and analyse the impact of these percentages on the survival of LUAD

Stain deconvolution of histology images via independent component analysis in the wavelet domain

With the ubiquity of digital slide scanners, histology image analysis is rapidly emerging as an active area of research. Several histology image analysis algorithms such as those for mitotic cell detection, nuclei segmentation and hormone receptors scoring depend on colour information obtained from images of the scanned slides. However, different standards followed by different labs and the technical variation among different scanners result in stain inconsistency in histology images. Thus, applications that use colour information may fail when they are applied to images with different appearance of stain colours. In this paper, we propose a novel method to estimate the so called stain matrix via independent component analysis in the wavelet domain for stain deconvolution in histology images. Experimental results demonstrate stable and more accurate stain deconvolution results as compared to other recently proposed algorithms. 2016 IEEE.Scopu

Distribution of neurovascular structures within the prostate gland and their relationship to complications after radical prostatectomy

BACKGROUND: Radical prostatectomy remains the main choice of treatment for prostate cancer. However, despite improvements in surgical techniques and neurovascular sparing procedures, rates of erectile dysfunction, and urinary incontinence remain variable. This is due, at least in part, to an incomplete understanding of neurovascular structures associated with the prostate. The objective of this study was to provide a comprehensive, detailed histological overview of the distribution of nerves and blood vessels within the prostate, facilitating subsequent correlation of prostatic neurovascular structures with patients' clinical outcomes after radical prostatectomy.METHODS: Neurovascular structures within the prostate were investigated in a total of 309 slides obtained from 15 patients who underwent non-nerve-sparing radical prostatectomy. Immunohistochemical staining was performed to identify and distinguish between parasympathetic and sympathetic nerves, whereas hematoxylin and eosin staining was used to identify blood vessels. The total number, density, and relative position of nerves and blood vessels were established using quantitative morphometry and illustrated using visualization approaches. Patient-specific outcome data were then used to establish whether the internal distribution of nerves and blood vessels within the prostate correlated with the nature and extent of complications after surgery. One-way analysis of variance tests and unpaired t tests were applied to establish statistically significant differences across the measured variables.RESULTS: Nerves and blood vessels were present across all prostatic levels and regions. However, their number and density varied considerably between regions. Assessment of the precise positioning of neurovascular structures revealed that the majority of nerve fibers were located within the dorsal and peripheral aspects of the gland. In contrast, blood vessels were predominantly located within ventral and dorsal midline regions. The number of intraprostatic nerves was found to be significantly lower in patients who recovered their continence within 12 months of surgery, compared to those whose recovery took 12 months or longer.CONCLUSION: We report an unexpected disconnect between the localization and positioning of nerve fibers and blood vessels within the prostate. Moreover, individual variability in the density of intraprostatic neurovascular structures appears to correlate with the successful recovery of urinary continence after radical prostatectomy, suggesting that differences in intrinsic neurovascular arrangements of the prostate influence postoperative outcomes.</p

Investigation of Mixed Convection in Spinning Nanofluid over Rotating Cone Using Artificial Neural Networks and BVP-4C Technique

The significance of back-propagated intelligent neural networks (BINs) to investigate the transmission of heat in spinning nanofluid over a rotating system is analyzed in this study. The buoyancy effect is incorporated along with the constant thermophysical properties of nanofluids. Levenberg–Marquardt intelligent networks (ANNLMBs) are employed to study heat transmission by using a trained artificial neural network. The system of highly non-linear flow governing partial differential equations (PDEs) is transformed into ordinary differential equations (ODEs) which is taken as a system model. This achieved system model is utilized to generate data set using the “Adams” method for distinct scenarios of heat transmission investigation in a spinning nanofluid over a rotating system for the implementation of the proposed ANNLMB. Additionally, with the help of training, testing, and validation, the approximate solution of heat transmission in a spinning nanofluid in a rotating system is obtained using a BNN-based solver. The generated reference data achieved employing the proposed artificial neural network based on a Levenberg–Marquardt intelligent network is distributed in the following manner: training at 82%, testing at 9%, and validation at 9%. Furthermore, MSE, histograms, and regression analyses are performed to depict and discuss the impact of the varying influence of key parameters, such as unsteadiness “s” in spinning flow, Prandtl number effect “pr”, the rotational ratio of nanofluid and cone α1 and buoyancy effect γ1 on velocities F′G and temperature Θ profiles. The mean square error confirms the accuracy of the achieved results. Prandtl number and unsteadiness decrease the temperature profile and thermal boundary layer of the rotating nanofluid

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson’s and Alzheimer’s. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases

Investigation of Mixed Convection in Spinning Nanofluid over Rotating Cone Using Artificial Neural Networks and BVP-4C Technique

The significance of back-propagated intelligent neural networks (BINs) to investigate the transmission of heat in spinning nanofluid over a rotating system is analyzed in this study. The buoyancy effect is incorporated along with the constant thermophysical properties of nanofluids. Levenberg–Marquardt intelligent networks (ANNLMBs) are employed to study heat transmission by using a trained artificial neural network. The system of highly non-linear flow governing partial differential equations (PDEs) is transformed into ordinary differential equations (ODEs) which is taken as a system model. This achieved system model is utilized to generate data set using the “Adams” method for distinct scenarios of heat transmission investigation in a spinning nanofluid over a rotating system for the implementation of the proposed ANNLMB. Additionally, with the help of training, testing, and validation, the approximate solution of heat transmission in a spinning nanofluid in a rotating system is obtained using a BNN-based solver. The generated reference data achieved employing the proposed artificial neural network based on a Levenberg–Marquardt intelligent network is distributed in the following manner: training at 82%, testing at 9%, and validation at 9%. Furthermore, MSE, histograms, and regression analyses are performed to depict and discuss the impact of the varying influence of key parameters, such as unsteadiness “s” in spinning flow, Prandtl number effect “pr”, the rotational ratio of nanofluid and cone α1 and buoyancy effect γ1 on velocities F′G and temperature Θ profiles. The mean square error confirms the accuracy of the achieved results. Prandtl number and unsteadiness decrease the temperature profile and thermal boundary layer of the rotating nanofluid

Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness. Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. Transfer learning has been applied to diabetic retinopathy classification with promising results. Pre-trained models, such as convolutional neural networks (CNNs), can be fine-tuned on a new dataset of retinal images to classify diabetic retinopathy. This manuscript aims at developing an automated scheme for diagnosing and grading DR and HR. The retinal image classification has been performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology has been proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques have been applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images has been performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification have been performed using transfer learning models. The segmented images were then classified using Modified ResNet 101 architecture. The performance for enhanced images has been evaluated on PSNR and shows better results as compared to the existing literature. The network is trained on more than 6000 images from MESSIDOR and ODIR datasets and achieves the classification accuracy of 98.72%

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available