scholarly journals Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

2021 ◽  
Vol 27 (2) ◽  
pp. 55-63
Author(s):  
Fikret Yalcinkaya ◽  
Ali Erbas
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Early Stage ◽  
Training Data ◽  
Data Set ◽  
Melanoma Diagnosis ◽  
Fuzzy Correlation ◽  
Deep Cnn ◽  
Aided Diagnosis ◽  
Logic System

Studies on the detection of early stage melanoma have recently gained significant interest. Computer aided diagnosis systems based on neural networks, machine learning, convolutional neural networks (CNNs), and deep learning help early stage detection considerably. The colour and shapes of the images created by the pixels are crucial for the CNNs, as the pixels and associated pictures are interrelated just as a person’s fingerprint is unique. By observing this relationship, the pixel values of each picture with its neighborhoods were determined by a fuzzy logic-based system and a unique fingerprint matrix named Fuzzy Correlation Map (FCov-Map) was produced. The fuzzy logic system has four inputs and one output. The advantage of CNNs trained with fuzzy covariance maps is to eliminate both the limited availability of medical grade training data and the need for extensive image preprocessing. The fuzzy logic output is fed to the pretrained AlexNet CNN algorithm. To deliver a reliable result, a deep CNN needs a large amount of data to process. However, to obtain and use the required sufficient data for diseases is not cost- and time-effective. Therefore, the suggested fuzzy logic-based fuzzy correlation map is tackling this issue to solve the limitedness of training CNN data set.

scholarly journals Localization of Scattering Objects Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 11
Author(s):  
Domonkos Haffner ◽  
Ferenc Izsák
Keyword(s):  
Neural Networks ◽  
Plane Waves ◽  
Measurement Data ◽  
Scattered Wave ◽  
Training Data ◽  
Data Set ◽  
Main Compound ◽  
Incident Plane ◽  
The Neural Networks ◽  
Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

Simple Convolutional-Based Models: Are They Learning the Task or the Data?

2021 ◽  
pp. 1-17
Author(s):  
Luis Sa-Couto ◽  
Andreas Wichert
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Training Data ◽  
Model Complexity ◽  
Data Sets ◽  
Simple Task ◽  
Data Set ◽  
Knowing That ◽  
Handwritten Digit ◽  
End To End

Abstract Convolutional neural networks (CNNs) evolved from Fukushima's neocognitron model, which is based on the ideas of Hubel and Wiesel about the early stages of the visual cortex. Unlike other branches of neocognitron-based models, the typical CNN is based on end-to-end supervised learning by backpropagation and removes the focus from built-in invariance mechanisms, using pooling not as a way to tolerate small shifts but as a regularization tool that decreases model complexity. These properties of end-to-end supervision and flexibility of structure allow the typical CNN to become highly tuned to the training data, leading to extremely high accuracies on typical visual pattern recognition data sets. However, in this work, we hypothesize that there is a flip side to this capability, a hidden overfitting. More concretely, a supervised, backpropagation based CNN will outperform a neocognitron/map transformation cascade (MTCCXC) when trained and tested inside the same data set. Yet if we take both models trained and test them on the same task but on another data set (without retraining), the overfitting appears. Other neocognitron descendants like the What-Where model go in a different direction. In these models, learning remains unsupervised, but more structure is added to capture invariance to typical changes. Knowing that, we further hypothesize that if we repeat the same experiments with this model, the lack of supervision may make it worse than the typical CNN inside the same data set, but the added structure will make it generalize even better to another one. To put our hypothesis to the test, we choose the simple task of handwritten digit classification and take two well-known data sets of it: MNIST and ETL-1. To try to make the two data sets as similar as possible, we experiment with several types of preprocessing. However, regardless of the type in question, the results align exactly with expectation.

Pancreatic cancer detection using EpiDetect signatures in plasma-derived cell free DNA in high-risk patients with new onset diabetes.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16265-e16265
Author(s):  
Gulfem Guler ◽  
Anna Bergamaschi ◽  
David Haan ◽  
Michael Kesling ◽  
Yuhong Ning ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Stage ◽  
Model Performance ◽  
Training Data ◽  
Diabetes Diagnosis ◽  
Whole Genome ◽  
Data Set ◽  
Independent Validation ◽  
Onset Diabetes ◽  
New Onset

e16265 Background: Pancreatic cancer (PaCa) is the third leading cause of cancer death in the United States despite its low incidence rate, owing to a 5-year survival rate of 10%. It is often asymptomatic in early stage, resulting in the majority of diagnoses occurring when cancer has already metastasized to distant organs. Late diagnosis deprives patients of potentially curative treatments such as surgery and impacts survival rates. Diabetes can be an early symptom of PaCa. Indeed, 25% of PaCa patients had a preceding diabetes diagnosis. Among all people with new onset diabetes (NOD), 0.85% will be diagnosed with PaCa within 3 years, which represents 6-8 fold increased risk for PaCa compared to the general population. Surveillance of the NOD population for PaCa presents an opportunity to shift PaCa diagnosis to earlier stage by finding it sooner. Methods: Whole blood was obtained from a cohort of 117 PaCa patients as well as 800 non-cancer controls with and without NOD. Plasma was processed to isolate cfDNA and 5hmC and low pass whole genome libraries were generated and sequenced. The EpiDetect assay combines 5hmC and whole genome sequencing data and were generated using Bluestar Genomics’s technology platform. Results: To investigate whether PaCa can be detected in plasma, we interrogated plasma-derived cfDNA epigenomic and genomic signal from PaCa patients and non-cancer controls. We first trained stacked ensemble models on PaCa and non-cancer samples utilizing 5hmC, fragmentation and CNV-based biomarkers from cfDNA. These models performed stably with a median of 72.8% sensitivity and 90.1% specificity measured across 25 outer fold iterations using the training data set, which was composed of 50% early stage (Stages I & II) disease. The final binomial ensemble model was trained using all of the training data, yielding an area under the receiver operating characteristic curve (auROC) of 0.9, with 75% sensitivity and 89% specificity. This model was then tested on an independent validation data set from 33 PaCa patients (24 with diabetes, 15 of which was NOD) and 202 non-cancer control patients (76 with diabetes, 51 of which was NOD) and yielded a classification performance auROC of 0.9 with 67% sensitivity at 92% specificity. Lastly, model performance in the subset of patient cohort with NOD only had an auROC of 0.87 with 60% sensitivity at 88% specificity. Conclusions: Our results indicate that 5hmC profiles along with CNV and fragmentation patterns from cfDNA can be used to detect PaCa in plasma-derived cfDNA. Overall, model performance was stable and consistent between the training and independent validation datasets. A larger clinical study is under development to investigate the utility of the model described in this pilot study in identifying occult PaCa within the NOD population, with the aim of shifting diagnosis to early stage and potentially improving patient outcomes.

NEURAL NETWORK METHODS FOR PLANAR IMAGE ANALYSIS IN AUTOMATED SCREENING SYSTEMS

2021 ◽  
Vol 4 (1) ◽  
pp. 71-79
Author(s):  
Borys Igorovych Tymchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Semantic Segmentation ◽  
Input Image ◽  
Training Data ◽  
Classification Task ◽  
Automated Screening ◽  
The Cost ◽  
Screening Systems

Nowadays, means of preventive management in various spheres of human life are actively developing. The task of automated screening is to detect hidden problems at an early stage without human intervention, while the cost of responding to them is low. Visual inspection is often used to perform a screening task. Deep artificial neural networks are especially popular in image processing. One of the main problems when working with them is the need for a large amount of well-labeled data for training. In automated screening systems, available neural network approaches have limitations on the reliability of predictions due to the lack of accurately marked training data, as obtaining quality markup from professionals is very expensive, and sometimes not possible in principle. Therefore, there is a contradiction between increasing the requirements for the precision of predictions of neural network models without increasing the time spent on the one hand, and the need to reduce the cost of obtaining the markup of educational data. In this paper, we propose the parametric model of the segmentation dataset, which can be used to generate training data for model selection and benchmarking; and the multi-task learning method for training and inference of deep neural networks for semantic segmentation. Based on the proposed method, we develop a semi-supervised approach for segmentation of salient regions for classification task. The main advantage of the proposed method is that it uses semantically-similar general tasks, that have better labeling than original one, what allows users to reduce the cost of the labeling process. We propose to use classification task as a more general to the problem of semantic segmentation. As semantic segmentation aims to classify each pixel in the input image, classification aims to assign a class to all of the pixels in the input image. We evaluate our methods using the proposed dataset model, observing the Dice score improvement by seventeen percent. Additionally, we evaluate the robustness of the proposed method to different amount of the noise in labels and observe consistent improvement over baseline version.

scholarly journals Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging

2019 ◽  
Vol 18 ◽  
pp. 153601211986353 ◽  
Author(s):  
Rui Zhang ◽  
Chao Cheng ◽  
Xuehua Zhao ◽  
Xuechen Li
Keyword(s):  
Pet Imaging ◽  
Lung Tumor ◽  
Training Data ◽  
Fine Tuning ◽  
Weighted Voting ◽  
Data Set ◽  
New Approach ◽  
Positron Emission ◽  
Voting Strategy ◽  
Aided Diagnosis

Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of “computer-aided diagnosis” is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region–Based Convolutional Neural Network (Mask R-CNN)–based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

scholarly journals Automatic Gully Detection: Neural Networks and Computer Vision

2020 ◽  
Vol 12 (11) ◽  
pp. 1743
Author(s):  
Artur M. Gafurov ◽  
Oleg P. Yermolayev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Satellite Images ◽  
Gully Erosion ◽  
Training Data ◽  
Russian Plain ◽  
Data Set ◽  
High Resolution Satellite Images

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine

2007 ◽  
Vol 11 (1) ◽  
pp. 4-10 ◽  
Author(s):  
Cao Thang ◽  
◽  
Eric W. Cooper ◽  
Yukinobu Hoshino ◽  
Katsuari Kamei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Oriental Medicine ◽  
The Neural Networks

In this paper, we present an application of soft computing into a decision support system RETS: Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are a diagnosis of rheumatic states, its explanations and herbal prescriptions. First, an outline of the proposed decision support system is described after considering rheumatic diagnoses and prescriptions by OM doctors. Next, diagnosis by fuzzy inference and prescription by neural networks are described. By fuzzy inference, RETS diagnoses the most appropriate rheumatic state in which the patient appears to be infected, then it gives a prescription written in suitable herbs with reasonable amounts based on neural networks. Training data for the neural networks is collected from experienced OM physicians and OM text books. Finally, we describe evaluations and restrictions of RETS.

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