Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization

Khan, Awais; Pin, Kuntha; Aziz, Ahsan; Han, Jung Woo; Nam, Yunyoung

doi:10.3390/s23156706

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…In [ 58 ], using the Inception ResNet-v2 model as an image feature extractor and combining classical classifiers, the classification process has been performed on the dataset of OCT images with five classes. Khan et al in [ 59 ] employed a method based on the use of pre-trained models of DenseNet201, Inception-v3, and ResNet50 neural networks, in which after optimizing the features extracted with the help of neural networks, k-nearest neighbors (KNN) and SVM classifiers are ultimately used to determine the class of each item. In [ 60 ], the hybrid ensemble deep network (HEDN) model based on the MobileNet-v2, ResNet50, and VGG16 models is presented to classify OCT retinal pathology images into four classes.…”

Section: Related Workmentioning

confidence: 99%

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Azizi,

Abhari,

Sajedi

2024

PLoS ONE

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Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

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Section: Related Workmentioning

confidence: 99%

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Azizi,

Abhari,

Sajedi

2024

PLoS ONE

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“…Hybrid deep learning models for retinal image classification have also been proposed recently. For instance, a hybrid deep learning model for OCT image classification was implemented by Khan et al [25]. They extracted retinal features from OCT images using three pre-trained deep learning models (DenseNet121, InceptionV3, and ResNet50), and ant colony optimization was used for best feature selection.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks

Bhandari,

Shahi,

Neupane

2023

J. Imaging

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Optical Coherence Tomography (OCT) is an imperative symptomatic tool empowering the diagnosis of retinal diseases and anomalies. The manual decision towards those anomalies by specialists is the norm, but its labor-intensive nature calls for more proficient strategies. Consequently, the study recommends employing a Convolutional Neural Network (CNN) for the classification of OCT images derived from the OCT dataset into distinct categories, including Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The average k-fold (k = 10) training accuracy, test accuracy, validation accuracy, training loss, test loss, and validation loss values of the proposed model are 96.33%, 94.29%, 94.12%, 0.1073, 0.2002, and 0.1927, respectively. Fast Gradient Sign Method (FGSM) is employed to introduce non-random noise aligned with the cost function’s data gradient, with varying epsilon values scaling the noise, and the model correctly handles all noise levels below 0.1 epsilon. Explainable AI algorithms: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are utilized to provide human interpretable explanations approximating the behaviour of the model within the region of a particular retinal image. Additionally, two supplementary datasets, namely, COVID-19 and Kidney Stone, are assimilated to enhance the model’s robustness and versatility, resulting in a level of precision comparable to state-of-the-art methodologies. Incorporating a lightweight CNN model with 983,716 parameters, 2.37×108 floating point operations per second (FLOPs) and leveraging explainable AI strategies, this study contributes to efficient OCT-based diagnosis, underscores its potential in advancing medical diagnostics, and offers assistance in the Internet-of-Medical-Things.

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“…Khan et al [23] proposed a hybrid model that utilizes modified pre-trained models such as Resnet50, Inception v3, and Densenet-201, along with an optimization technique. Despite the increase in the number of parameters, this approach achieved an accuracy of 97.1%.…”

Section: Introductionmentioning

confidence: 99%

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Prabha,

Venkatesan,

Fathimal

et al. 2024

Biomed. Phys. Eng. Express

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Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

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Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization

Cited by 19 publications

References 43 publications

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

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