Support Vector Machine Based Method for Automatic Detection of Diabetic Eye Disease using Thermal Images

Selvathi, D.; Suganya, K.

doi:10.1109/iciict1.2019.8741450

Cited by 25 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computer vision is a field within artificial intelligence that focuses on developing algorithms and models for interpreting visual data. In this study, the primary focus is on the application of deep learning techniques, specifically convolutional neural networks (CNNs) [3][4][5], for automating the detection and categorization of ocular disorders from retinal images. This research topic is significant due to the growing need for accurate and efficient methods of identifying eye disorders, as well as the potential of machine learning to enhance diagnostic precision and reduce human error.…”

Section: Methodsmentioning

confidence: 99%

Optimized Screening of Glaucoma using Fundus Images and Deep Learning

S.,

B. K.

2023

IJRITCC

View full text Add to dashboard Cite

Diabetic retinopathy, glaucoma, and age-related macular degeneration are among the leading causes of global visual loss. Early detection and diagnosis of these conditions are crucial to reduce vision loss and improve patient outcomes. In recent years, deep learning algorithms have shown great potential in automating the diagnosis and categorization of eye disorders using medical photos. For this purpose, the ResNet-50 architecture is employed in a deep learning-based strategy. The approach involves fine-tuning a pre-trained ResNet-50 model using over 5,000 retinal pictures from the ODIR dataset, covering ten different ocular diseases. To enhance the model's generalization performance and avoid overfitting, various data augmentation techniques are applied to the training data. The model successfully detects glaucoma-related ocular illnesses, including cataract, diabetic retinopathy, and healthy eyes. Performance evaluation using metrics like accuracy, precision, recall, and F1-score shows that the model achieved 92.60% accuracy, 93.54% precision, 91.60% recall, and an F1-score of 91.68%. These results indicate that the proposed strategy outperforms many state-of-the-art approaches in the detection and categorization of eye disorders. This success underscores the potential of deep learning-based methods in automated ocular illness identification, facilitating early diagnosis and timely treatment to ultimately improve patient outcomes.

show abstract

Section: Methodsmentioning

confidence: 99%

Optimized Screening of Glaucoma using Fundus Images and Deep Learning

S.,

B. K.

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…In 2019, D. Selvathi and K. Suganya [77] proposed a model based on the ML algorithm to predict diabetic patients using thermographic eye images and incorporate the impact of the eye structure's thermal variation abnormality as a diagnostic imaging tool that is useful for clinical diagnosis by ophthalmologists. Thermal images are preprocessed, and then texture feature depends on Gray Level Co-occurrence Matrix (GLCM) from gray images.…”

Section: H Deep Learningmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

158

View full text Add to dashboard Cite

Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

show abstract

“…Thermal images contain two types of features which are textural and statistical features. Texture feature measure the relationship among the pixels in local area, reflecting the changes of image gray levels [24]. This is why the images were converted to grey scale so that their Gray-Level Co-Occurrence Matrix (GLCM) properties can be used in feature extraction.…”

Section: B Feature Extractionmentioning

confidence: 99%

An Intelligent Approach for Detecting Palm Trees Diseases using Image Processing and Machine Learning

Alaa¹,

Waleed²,

Samir³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Today's palm trees diseases which cause a huge loss in production are extremely hard to detect either because these diseases are hidden inside the texture of the palm itself and cannot be seen by naked eyes or because it appears on its leaves which are hardly examined due to how far they really are from the ground. In this paper we're interested in detecting three of the most common diseases threatening palms today, Leaf Spots, Blight Spots and Red Palm Weevil. Diagnosis of these diseases are done by capturing normal and thermal images of palm trees then, image processing techniques were applied to the acquired images. Two classifiers were used, CNN to differentiate between Leaf Spots and Blight Spots diseases and SVM for Red Palm Weevil pest. The results for CNN and SVM algorithms showed a success rate of accuracy ratio 97.9% and 92.8% respectively, these results are considered to be the best results in this domain as far as we know. The paper also includes the first gathered thermal images dataset for palms infected with Red Palm Weevil and healthy palms as well.

show abstract

Support Vector Machine Based Method for Automatic Detection of Diabetic Eye Disease using Thermal Images

Cited by 25 publications

References 8 publications

Optimized Screening of Glaucoma using Fundus Images and Deep Learning

Optimized Screening of Glaucoma using Fundus Images and Deep Learning

The Role of Machine Learning Algorithms for Diagnosing Diseases

An Intelligent Approach for Detecting Palm Trees Diseases using Image Processing and Machine Learning

Contact Info

Product

Resources

About