scholarly journals Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 11
Author(s):  
Prasanalakshmi Balaji ◽  
Kumarappan Chidambaram
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Disease Diagnosis ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Clinical Cancer Research ◽  
Cancer Disease ◽  
Social Spider ◽  
Life Threatening ◽  
Sensitivity Specificity

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

scholarly journals Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network

2021 ◽  
Author(s):  
Prasanalakshmi Balaji ◽  
Kumarappan Chidambaram
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Disease Diagnosis ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Clinical Cancer Research ◽  
Cancer Disease ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Sensitivity Specificity

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

scholarly journals Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Saritha Balasubramaniyan ◽  
Vijay Jeyakumar ◽  
Deepa Subramaniam Nachimuthu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Performance Metrics ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
High Rate ◽  
Diabetes Diagnosis ◽  
Operating Characteristics ◽  
High Glucose Level ◽  
Sensitivity Specificity

AbstractDiabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.

scholarly journals Canine Olfactory Detection of a Non-Systemic Phytobacterial Citrus Pathogen of International Quarantine Significance

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1269 ◽  
Author(s):  
Timothy Gottwald ◽  
Gavin Poole ◽  
Earl Taylor ◽  
Weiqi Luo ◽  
Drew Posny ◽  
...  
Keyword(s):  
Bacterial Cell ◽  
Performance Metrics ◽  
Disease Incidence ◽  
Citrus Canker ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
Bacterial Cells ◽  
Tree Detection ◽  
Low Concentrations ◽  
Sensitivity Specificity

For millennia humans have benefitted from application of the acute canine sense of smell to hunt, track and find targets of importance. In this report, canines were evaluated for their ability to detect the severe exotic phytobacterial arboreal pathogen Xanthomonas citri pv. citri (Xcc), which is the causal agent of Asiatic citrus canker (Acc). Since Xcc causes only local lesions, infections are non-systemic, limiting the use of serological and molecular diagnostic tools for field-level detection. This necessitates reliance on human visual surveys for Acc symptoms, which is highly inefficient at low disease incidence, and thus for early detection. In simulated orchards the overall combined performance metrics for a pair of canines were 0.9856, 0.9974, 0.9257 and 0.9970, for sensitivity, specificity, precision, and accuracy, respectively, with 1–2 s/tree detection time. Detection of trace Xcc infections on commercial packinghouse fruit resulted in 0.7313, 0.9947, 0.8750, and 0.9821 for the same performance metrics across a range of cartons with 0–10% Xcc-infected fruit despite the noisy, hot and potentially distracting environment. In orchards, the sensitivity of canines increased with lesion incidence, whereas the specificity and overall accuracy was >0.99 across all incidence levels; i.e., false positive rates were uniformly low. Canines also alerted to a range of 1–12-week-old infections with equal accuracy. When trained to either Xcc-infected trees or Xcc axenic cultures, canines inherently detected the homologous and heterologous targets, suggesting they can detect Xcc directly rather than only volatiles produced by the host following infection. Canines were able to detect the Xcc scent signature at very low concentrations (10,000× less than 1 bacterial cell per sample), which implies that the scent signature is composed of bacterial cell volatile organic compound constituents or exudates that occur at concentrations many fold that of the bacterial cells. The results imply that canines can be trained as viable early detectors of Xcc and deployed across citrus orchards, packinghouses, and nurseries.

scholarly journals Acute Myelogenous Leukemia Detection using Circumventing Ant Colony Optimization based Convolutional Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 705-712
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Acute Myelogenous Leukemia ◽  
Performance Metrics ◽  
Myelogenous Leukemia ◽  
Bench Mark ◽  
Rapid Progression ◽  
Cancer Disease ◽  
Main Challenge ◽  
Acute Myelogenous

Acute Myelogenous Leukemia (AML) is a type of disease associated with acute leukemia which is getting increased in both children’s and adults. AML falls under the category of cancer disease. The term acute in AML indicates rapid progression of disease in human body. The main challenge of medical field in vision of computer and multimedia is texture and color between various categories. The variation in texture and color attributes makes the classification task a tedious. Deep learning has shown its dazzling performance in various streams, which includes classification too. The objective of image classification is to differentiate the subcategories that belong to same basic-level category. The main objective of this paper is to propose bioinspired based on convolutional neural network to classify the microscopic blood images for AML. This paper has utilized bioinspired concept to extract the features more reliably. Bench mark performance metrics were chosen to evaluate the proposed classifier against the previous classifiers based on two parameters. The results indicate that the proposed classifiers has outperformed the previous works towards the classification of AML.

Boosted neural network ensemble classification for lung cancer disease diagnosis

2019 ◽  
Vol 80 ◽  
pp. 579-591 ◽  
Author(s):  
Jafar A. ALzubi ◽  
Balasubramaniyan Bharathikannan ◽  
Sudeep Tanwar ◽  
Ramachandran Manikandan ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Disease Diagnosis ◽  
Ensemble Classification ◽  
Neural Network Ensemble ◽  
Cancer Disease

scholarly journals Angiostrongylus costaricensis infection in Martinique, Lesser Antilles, from 2000 to 2017

Parasite ◽  
2018 ◽  
Vol 25 ◽  
pp. 22 ◽  
Author(s):  
Céline Dard ◽  
Duc Nguyen ◽  
Charline Miossec ◽  
Katia de Meuron ◽  
Dorothée Harrois ◽  
...  
Keyword(s):  
Descriptive Analysis ◽  
Case Series ◽  
Epidemiological Studies ◽  
Human Infection ◽  
University Hospital ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Intestinal Lesions ◽  
Life Threatening ◽  
The University

Human abdominal angiostrongyliasis (HAA) is a parasitic disease caused by the accidental ingestion of the nematode Angiostrongylus costaricensis in its larval form. Human infection can lead to severe ischemic and inflammatory intestinal lesions, sometimes complicated by life-threatening ileal perforations. Only one case had been reported in Martinique, an Island in the French Antilles, in 1988. We retrospectively reviewed the medical charts of patients diagnosed with abdominal angiostrongyliasis at the University Hospital of Martinique between 2000 and 2017. The objectives of this study were to evaluate the incidence and perform a descriptive analysis of the clinical, biological, radiological, and histopathological features of HAA in Martinique. Two confirmed cases and two probable cases were identified in patients aged from 1 to 21 years during the 18-year period, with an estimated incidence of 0.2 cases per year (0.003 case/year/100.000 inhabitants (IC95% = 0.00–0.05)). All patients presented with abdominal pain associated with high blood eosinophilia (median: 7.24 G/L [min 4.25; max 52.28 G/L]). Two developed ileal perforation and were managed by surgery, with diagnostic confirmation based on histopathological findings on surgical specimens. The other two cases were probable, with serum specimens reactive to Angiostrongylus sp. antigen in the absence of surgery. All cases improved without sequelae. The description of this case series highlights the need to increase awareness of this life-threatening disease in the medical community and to facilitate access to specific diagnostic tools in Martinique. Environmental and epidemiological studies are needed to broaden our knowledge of the burden of this disease.

scholarly journals Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data

2021 ◽  
Vol 12 (5) ◽  
pp. 67-83
Author(s):  
Ajay Dev ◽  
Sanjay Kumar Malik
Keyword(s):  
Neural Network ◽  
Medical Imaging ◽  
Deep Neural Network ◽  
Artificial Bee Colony ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Diagnostic Tools ◽  
Healthcare Data ◽  
Bee Colony ◽  
Learning Techniques

The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.

A Computer Aided Diagnostic Tool for the Detection of Uterine Fibroids

2013 ◽  
Vol 2 (1) ◽  
pp. 26-38
Author(s):  
N. Sriraam ◽  
L. Vinodashri
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Uterine Fibroid ◽  
Diagnostic Tool ◽  
Probabilistic Neural Network ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Morphological Operations ◽  
Statistical Features ◽  
Computer Aided

The integration of information technology with biomedicine has provided viable diagnostic tools to the medical community. Such computer aided procedures fastens the clinical decision process without any hurdle. Among different medical imaging modalities, Ultrasonic Imaging plays a vital role in detecting gynecological pathologies. Of importance, Uterine fibroid detection requires significant attention where symptoms such as, infertility and miscarriage can be predicted. This paper suggests an automated computer aided diagnostic tool for the detection of uterine fibroid. Gabor wavelets are applied for texture segmentation and statistical features such as mean, variance, standard deviation, skewness, kurtosis, Eigen values, GLCM contrast and energy are extracted from the user defined region of interest (ROI). The qualitative procedure is examined using the morphological operations and gray level intensity variations. Two neural network models, multilayer perceptron neural network (MLP) and probabilistic neural network (PNN) are applied to classify the normal and fibroid uterus image. It is found from the experimental computer simulation, a classification accuracy of 97.25% is obtained using combinational statistical features, mean and standard deviation with PNN classifier. It can be concluded that the proposed tool can applied as an efficient Medical Expert System for diagnosing the Ultrasonic Uterus images.

A Workflow for the Performance of the Differential Ovarian Follicle Count Using Deep Neuronal Networks

2020 ◽  
pp. 019262332096913
Author(s):  
Eleonora Carboni ◽  
Heike Marxfeld ◽  
Hanati Tuoken ◽  
Christian Klukas ◽  
Till Eggers ◽  
...  
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Ovarian Follicle ◽  
Network Architectures ◽  
The Neural Network ◽  
Good Laboratory ◽  
Different Depths ◽  
Set Up ◽  
Sensitivity Specificity ◽  
And Storage

In order to automate the counting of ovarian follicles required in multigeneration reproductive studies performed in the rat according to Organization for Economic Co-operation and Development guidelines 443 and 416, the application of deep neural networks was tested. The manual evaluation of the differential ovarian follicle count is a tedious and time-consuming task that requires highly trained personnel. In this regard, deep learning outputs provide overlay pictures for a more detailed documentation, together with an increased reproducibility of the counts. To facilitate the planned good laboratory practice (GLP) validation a workflow was set up using MLFlow to make all steps from generating of scans, training of the neural network, uploading of study images to the neural network, generation and storage of the results in a compliant manner controllable and reproducible. PyTorch was used as main framework to build the Faster region-based convolutional neural network for the training. We compared the performances of different depths of ResNet models with specific regard to the sensitivity, specificity, accuracy of the models. In this paper, we describe all steps from data labeling, training of networks, and the performance metrics chosen to evaluate different network architectures. We also make recommendation on steps, which should be taken into consideration when GLP validation is aimed for.

scholarly journals Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6977 ◽  
Author(s):  
Sivaramakrishnan Rajaraman ◽  
Stefan Jaeger ◽  
Sameer K. Antani
Keyword(s):  
Large Scale ◽  
Blood Smear ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Disease Diagnosis ◽  
Training Data ◽  
Diagnostic Tools ◽  
Thin Blood Smear ◽  
Model Ensemble

Background Malaria is a life-threatening disease caused by Plasmodium parasites that infect the red blood cells (RBCs). Manual identification and counting of parasitized cells in microscopic thick/thin-film blood examination remains the common, but burdensome method for disease diagnosis. Its diagnostic accuracy is adversely impacted by inter/intra-observer variability, particularly in large-scale screening under resource-constrained settings. Introduction State-of-the-art computer-aided diagnostic tools based on data-driven deep learning algorithms like convolutional neural network (CNN) has become the architecture of choice for image recognition tasks. However, CNNs suffer from high variance and may overfit due to their sensitivity to training data fluctuations. Objective The primary aim of this study is to reduce model variance, improve robustness and generalization through constructing model ensembles toward detecting parasitized cells in thin-blood smear images. Methods We evaluate the performance of custom and pretrained CNNs and construct an optimal model ensemble toward the challenge of classifying parasitized and normal cells in thin-blood smear images. Cross-validation studies are performed at the patient level to ensure preventing data leakage into the validation and reduce generalization errors. The models are evaluated in terms of the following performance metrics: (a) Accuracy; (b) Area under the receiver operating characteristic (ROC) curve (AUC); (c) Mean squared error (MSE); (d) Precision; (e) F-score; and (f) Matthews Correlation Coefficient (MCC). Results It is observed that the ensemble model constructed with VGG-19 and SqueezeNet outperformed the state-of-the-art in several performance metrics toward classifying the parasitized and uninfected cells to aid in improved disease screening. Conclusions Ensemble learning reduces the model variance by optimally combining the predictions of multiple models and decreases the sensitivity to the specifics of training data and selection of training algorithms. The performance of the model ensemble simulates real-world conditions with reduced variance, overfitting and leads to improved generalization.

Sign in / Sign up

Export Citation Format

Share Document