Classification of Eye Diseases and Detection of Cataract using Digital Fundus Imaging (DFI) and Inception-V4 Deep Learning Model

Raza, Ali; Khan, Misha Urooj; Saeed, Zubair; Samer, Sana; Mobeen, Areeba; Samer, Aqsa

doi:10.1109/fit53504.2021.00034

Cited by 20 publications

(7 citation statements)

References 15 publications

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“…Combining their respective strengths in feature extraction, the fusion method combines the learned representa-tions from both networks. We used the torchvision library to load the InceptionV3 and DenseNet121 models that have already been trained and perform well in feature extraction, they are often used for image classification tasks [22][23][24] for various applications. To benefit from their pre-trained weights for feature extraction, we froze the parameters of both InceptionV3 and DenseNet121 during training.…”

Section: Dual Dcnn Modelmentioning

confidence: 99%

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Saeed,

Bouhali,

et al. 2024

Bioengineering

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Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.

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Section: Dual Dcnn Modelmentioning

confidence: 99%

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Saeed,

Bouhali,

et al. 2024

Bioengineering

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“…To overcome some of the shortcomings of standard CNN architectures like overfitting, high computational burden, and fading gradient problem, Raza et al [164] [166] proposed an attention network that focuses on global features as well as local features before final grading.…”

Section: Cataract Diagnosismentioning

confidence: 99%

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images

et al. 2022

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In recent years, there has been an unprecedented growth in computer vision and deep learning implementation owing to the exponential rise of computation infrastructure. The same was also reflected in retinal image analysis and successful artificial intelligence models were developed for various retinal disease diagnoses using a wide variety of visual markers obtained from eye fundus images. This article presents a comprehensive study of different deep learning strategies employed in recent times for the diagnosis of five major eye diseases, i.e., Diabetic retinopathy, Glaucoma, age-related macular degeneration, Cataract, and Retinopathy of prematurity. This article is organized according to the deep learning implementation process pipeline, where commonly used datasets, evaluation metrics, image pre-processing techniques, and deep learning backbone models are first illustrated followed by an extensive review of different strategies for each of the five mentioned retinal diseases is presented. Finally, this article summarizes eight major research directions available in the field of retinal disease diagnosis and outlines key challenges and future scope for the present research community.

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“…Digital images, videos, and other visual inputs can be used to extract information used by computers and systems in computer vision. These systems respond in response to the input [35,36,37]. Aging affects humans' lives severely.…”

Section: Computer Vision Based Fall Detectionmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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Classification of Eye Diseases and Detection of Cataract using Digital Fundus Imaging (DFI) and Inception-V4 Deep Learning Model

Cited by 20 publications

References 15 publications

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

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