scholarly journals Endomicroscopy Image Recognition using Ensemble Neural network with Contrast Limited Adaptive Histogram Equalisation

2020 ◽  
Vol 9 (4) ◽  
pp. 44-49
Keyword(s):  
Neural Network ◽  
Binary Classification ◽  
Histogram Equalization ◽  
Poor Quality ◽  
Generalization Error ◽  
Adaptive Histogram Equalization ◽  
Ensemble Neural Network ◽  
Small Tool

Endomicroscopy is a small tool used for cancer diagnosis, this enables in-vivo imaging at microscopic resolution closely to histology image during endoscopic procedures and captured image within the dataset has high imaging quality resulting in an inequality between moral and poor-quality images. There's no clear demonstration of the artifacts in an endomicroscopy producer. During this proposed method, the ensemble neural network (ENN) approach models to scale back the variance of predictions and reduce generalization error with contrast limited adaptive histogram equalization (CLAHE) algorithm were used to recover the image pixel balancing. Binary classification of accuracy 98.79% has been achieved.

scholarly journals Endomicroscopy Image Recognition using Ensemble Neural network with Contrast Limited Adaptive Histogram Equalisation

2020 ◽  
Vol 9 (4) ◽  
pp. 44-49
Keyword(s):  
Neural Network ◽  
Binary Classification ◽  
Histogram Equalization ◽  
Poor Quality ◽  
Generalization Error ◽  
Adaptive Histogram Equalization ◽  
Ensemble Neural Network ◽  
Small Tool

Endomicroscopy is a small tool used for cancer diagnosis, this enables in-vivo imaging at microscopic resolution closely to histology image during endoscopic procedures and captured image within the dataset has high imaging quality resulting in an inequality between moral and poor-quality images. There's no clear demonstration of the artifacts in an endomicroscopy producer. During this proposed method, the ensemble neural network (ENN) approach models to scale back the variance of predictions and reduce generalization error with contrast limited adaptive histogram equalization (CLAHE) algorithm were used to recover the image pixel balancing. Binary classification of accuracy 98.79% has been achieved.

Classification of Spine Image from MRI Image Using Convolutional Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 2987-2995
Author(s):  
Geetha Raja ◽  
J. Mohan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Histogram Equalization ◽  
Spine Tumor ◽  
Affected Area ◽  
Adaptive Histogram Equalization ◽  
Tumor Region ◽  
Model Algorithm ◽  
Mri Image

The spine tumor is a fast-growing abnormal cell in the spinal canal or vertebrae of the spine, it affected many people. Thousands of researchers have focused on this disease for better understanding of tumor classification to provide more effective treatment to the patients. The main objective of this paper is to form a methodology for classification of spine image. We proposed an efficient and effective method that helpful for classifying the spine image and identified tumor region without any human assistance. Basically, Contrast Limited Adaptive Histogram Equalization used to improve the contrast of spine images and to eliminate the effect of unwanted noise. The proposed methodology will classify spine images as Normal or Abnormal using Convolutional Neural Network (CNN) model algorithm. The CNN model can classify spine image as Normal or Abnormal with 99.4% Accuracy, 94.5% Sensitivity, 95.6% Precision, and 99.9% specificity. Compared with the previous existing methods, our proposed solution achieved the highest performance in terms of classification based on the spine dataset. From the experimental results performed on the different images, it is clear that the analysis for the spine tumor detection is fast and accurate when compared with the manual detection performed by radiologists or clinical experts, So, anyone can easily identify the tumor affected area also determine abnormal images.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals An Efficient Segmentation based Classification of Diabetic Retinopathy Identification using CLAHE with ResNet Model

2019 ◽  
Vol 8 (9) ◽  
pp. 2277-2283
Keyword(s):  
Diabetic Retinopathy ◽  
Histogram Equalization ◽  
Classification Model ◽  
Diabetic Patients ◽  
Detection Model ◽  
Adaptive Histogram Equalization ◽  
Active Population ◽  
Classifier Performance ◽  
Timely Treatment

Diabetic retinopathy (DR) is a widespread problem for diabetic patient and it has been a main reason for blindness in the active population. Several difficulties faced by diabetic patients because of DR can be eliminated by properly maintaining the blood glucose and by timely treatment. As the DR comes with different stages and varying difficulties, it is hard to DR and also it is time consuming. In this paper, we develop an automated segmentation based classification model for DR. Initially, the Contrast limited adaptive histogram equalization (CLAHE) is used for segmenting the images. Later, residual network (ResNet) is employed for classifying the images into different grades of DR. For experimental analysis, the dataset is derived from Kaggle website which is open source platform that attempts to build DR detection model. The highest classifier performance is attained by the presented model with the maximum accuracy of 83.78, sensitivity of 67.20 and specificity of 89.36 over compared models

scholarly journals A neural network framework for binary classification of radar detections

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jabran Akhtar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
High Probability ◽  
Binary Classification ◽  
Guard Cells ◽  
Probability Of Detection ◽  
Constant False Alarm Rate

AbstractA desired objective in radar target detection is to satisfy two very contradictory requirements: offer a high probability of detection with a low false alarm rate. In this paper, we propose the utilization of artificial neural networks for binary classification of targets detected by a depreciated detection process. It is shown that trained neural networks are capable of identifying false detections with considerable accuracy and can to this extent utilize information present in guard cells and Doppler profiles. This allows for a reduction in the false alarm rate with only moderate loss in the probability of detection. With an appropriately designed neural network, an overall improved system performance can be achieved when compared against traditional constant false alarm rate detectors for the specific trained scenarios.

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