Detection of Diabetic Retinopathy (DR) Using Convolutional Neural Network (CNN) and Multiple Classifier Techniques in Machine Learning

2022 ◽  
pp. 201-209
Author(s):  
Umesh Anandrao Patil ◽  
Sanjeev J. Wagh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Proliferative Diabetic Retinopathy ◽  
Automated System ◽  
Early Stages ◽  
Learning Classifier ◽  
Multiple Classifier

The medical industry has advanced in a manner where high end technologies are used for early detection and analysis of diseases that are hard to encounter with normal procedures of the medical field. One such disease is diabetic retinopathy (DR) further classified as non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) conditions. Early detection of NPDR is a challenging task, and it requires examination of fundus images in an amplified manner. To overcome these early detection of DR, the authors propose an automated system that will be using machine learning classifier techniques with combination of convolutional neural network (CNN) to self-train the system and detect the early stages of retinal scans by feature extraction and use of existing retinal scan databases. Hence, the system will eliminate the human flaw of inability to detect early DR in diagnosis and will help us treat the patient in early stages.

scholarly journals Diabetic Retinopathy Severity Detection using Convolutional Neural Network

2020 ◽  
Vol 32 ◽  
pp. 01012
Author(s):  
Mayank Shete ◽  
Saahil Sabnis ◽  
Srijan Rai ◽  
Gajanan Birajdar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Feature Learning ◽  
Eye Diseases ◽  
Accuracy Rate ◽  
Irreversible Damage ◽  
Computer Aided

Diabetic Retinopathy is one of the most prominent eye diseases and is the leading cause of blindness amongst adults. Automatic detection of Diabetic Retinopathy is important to prevent irreversible damage to the eye-sight. Existing feature learning methods have a lesser accuracy rate in computer aided diagnostics; this paper proposes a method to further increase the accuracy. Machine learning can be used effectively for the diagnosis of this disease. CNN and transfer learning are used for the severity classification and have achieved an accuracy of 73.9 percent. The use of XGBoost classifier yielded an accuracy of 76.5 percent.

scholarly journals Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy

2019 ◽  
Vol 9 (2) ◽  
pp. 141
Author(s):  
Hartanto Ignatius ◽  
Ricky Chandra ◽  
Nicholas Bohdan ◽  
Abdi Dharma
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Contrast Enhancement ◽  
Neural Network Model ◽  
Reasonable Accuracy ◽  
Deep Architecture

<p class="JGI-AbstractIsi">Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.</p>

Lung Cancer Detection Using Deep Convolutional Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 13-20
Author(s):  
Syed Farhan Hyder Abidi ◽  
Sumukhi T. ◽  
Vinod Kumar H. ◽  
Santhosh B.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Lung Cancer ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Neural System ◽  
Malignant Growth ◽  
The World ◽  
Lung Cancer Detection

Lung malignant growth is one of the most threatening ailments affecting most of the nations in the world, and detection in earlier stages has been a challenge. Early detection can help in saving many lives. This paper shows a methodology that uses a convolutional neural network (CNN) in machine learning for the detection of tumours in the lung. The specificity of the model is desirable and dependable and increasingly productive in contrast to the accuracy shown by conventional neural system frameworks.

scholarly journals Efficient Breast Cancer Classification Using Histopathological Images and a Simple VGG

2022 ◽  
Vol 29 (1) ◽  
pp. 102-114
Author(s):  
Marcelo Luis Rodrigues Filho ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Severe Condition ◽  
Learning Capabilities ◽  
Histopathological Images ◽  
Deadly Disease

Breast cancer is the second most deadly disease worldwide. This severe condition led to 627,000 people dying in 2018. Thus, early detection is critical for improving the patients' lifetime or even curing them. In this context, we can appeal to Medicine 4.0, which exploits machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.

scholarly journals Efficient Breast Cancer Classification Using Histopathological Images and a Simpler VGG

2021 ◽  
Author(s):  
Marcelo Luis Rodrigues Filho ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Severe Condition ◽  
Learning Capabilities ◽  
Histopathological Images ◽  
Deadly Disease

Breast cancer is the second most deadly disease worldwide. This severe condition led 627,000 people to die in 2018. Thus, early detection is critical for improving the patients' lifetime or even cure them. In this context, we can appeal to Medicine 4.0 that exploits the machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.

scholarly journals Deep Learning untuk Klasifikasi Diabetic Retinopathy menggunakan Model EfficientNet

2020 ◽  
Vol 8 (3) ◽  
pp. 693
Author(s):  
SYAMSUL RIZAL ◽  
NUR IBRAHIM ◽  
NOR KUMALASARI CAESAR PRATIWI ◽  
SOFIA SAIDAH ◽  
RADEN YUNENDAH NUR FU’ADAH
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Keywords Diabetic Retinopathy ◽  
Complications Of Diabetes ◽  
Diabetes Melitus ◽  
Learning By Using

ABSTRAKDiabetic Retinopathy merupakan penyakit yang dapat mengakibatkan kebutaan mata yang disebabkan oleh adanya komplikasi penyakit diabetes melitus. Oleh karena itu mendeteksi secara dini sangat diperlukan untuk mencegah bertambah parahnya penyakit tersebut. Penelitian ini merancang sebuah sistem yang dapat mendeteksi Diabetic Retinopathy berbasis Deep Learning dengan menggunakan Convolutional Neural Network (CNN). EfficientNet model digunakan untuk melatih dataset yang telah di pre-prosesing sebelumnya. Hasil dari penelitian tersebut didapatkan akurasi sebesar 79.8% yang dapat mengklasifikasi 5 level penyakit Diabetic Retinopathy.Kata kunci: Diabetic Retinopathy, Deep Learning, CNN, EfficientNet, Diabetic Classification ABSTRACTDiabetic Retinopathy is a diseases which can cause blindness in the eyes because of the complications of diabetes mellitus. Therefore, an early detection for this diseases is very important to prevent the diseases become severe. This research builds the system which can detect the Diabetic Retinopathy based on Deep Learning by using Convolutional Neural Network (CNN). EfficientNet model is used to trained the dataset which have been pre-prossed. The result shows that the system can clasiffy the 5 level of Diabetic Retinopathy with accuracy 79.8%. Keywords: Diabetic Retinopathy, Deep Learning, CNN, EfficientNet, Diabetic Classification

scholarly journals Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy

2019 ◽  
Vol 9 (2) ◽  
pp. 141-150
Author(s):  
Hartanto Ignatius ◽  
Ricky Chandra ◽  
Nicholas Bohdan ◽  
Abdi Dharma
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Contrast Enhancement ◽  
Neural Network Model ◽  
Reasonable Accuracy ◽  
Deep Architecture

Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

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