Lightweight Convolutional Neural Network Model Research Articles

Thermal video-based hand gestures recognition using lightweight CNN

AbstractHand gesture recognition has gained a lot of attention in computer vision due to multiple applications. Further, most of the existing works utilized RGB data for hand gesture recognition. However, RGB cameras mainly depend on lighting, angles, and other factors including skin color which impacts the accuracy. Thus, we propose a methodology for video hand gesture recognition using thermal data in this work. Initially, we created a dataset of short video sequences captured from a thermal camera. Thereafter, a lightweight convolutional neural network model (CNN) is proposed for hand gesture recognition. Further, the performance of the proposed CNN model is evaluated on different sizes of the dataset consisting of 15, 10, and 5 frames per sequence. Results show that the proposed model achieves an accuracy of $$97\% \pm (0.05)$$, $$96\% \pm (0.05)$$, and $$87\% \pm (0.1)$$ on the dataset consisting of 15, 10, and 5 frames per sequence, respectively.

Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

A two-stage accelerated search strategy for large-scale multi-objective evolutionary algorithm

Since large-scale multi-objective problems (LSMOPs) have huge decision variables, the traditional evolutionary algorithms are facing difficulties of low exploitation efficiency and high exploration costs in solving LSMOPs. Therefore, this paper proposes an evolutionary strategy based on two-stage accelerated search optimizers (ATAES). Specifically, a convergence optimizer is devised in the first stage, while a three-layer lightweight convolutional neural network model is built, and the population is homogenized into two subsets, the diversity subset, and the convergence subset, which serve as input nodes and the expected output nodes of the neural network, respectively. Then, by constantly backpropagating the gradient, a satisfactory individual will be produced. Once exploitation stagnation is discovered in the first phase, the second phase will be run, where a diversity optimizer using a differential optimization algorithm with opposite learning is suggested to increase the exploration range of candidate solutions and thereby increase the population's diversity. Finally, to validate the algorithm's performance, on multi-objective LSMOP and DTLZ benchmark suits with decision variable quantities of 100, 300, 500, and 1000, the ATAES demonstrated its superiority with other advanced multi-objective evolutionary algorithms.

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

The classification of mushroom species presents significant ecologic and health-related challenges; advancement in classification techniques is required to gain reliable identifications. This study aims to explain a methodology that was devised and evaluated in the development of a novel, lightweight Convolutional Neural Network (CNN) designed specifically for the task of mushroom classification. The paper provides a custom CNN model that is computationally cost-effective and capable of high-precision classification, fit for real-time usage. Hence, the proposed model was evaluated on this dataset of curated mushroom images with traditional classifiers and state-of-the-art CNN architectures, such as EfficientNet-B7, ResNet50, InceptionV3, and MobileNetV2. The custom model is depth-wise separations engineered in such a way that while they reduce the computational load, they don't compromise the effectiveness of the model. The custom model achieved a test score of 0.68, which is moderate compared to more established models such as EfficientNet-B7 or ResNet50. This approach helps the model function effectively even on platforms having low computational resources. A comprehensive evaluation reveals that a custom CNN has reasonable accuracy in the identification of different mushroom species vis-à-vis existing models, but also significantly lightens the classification process.

A lightweight CNN model for pepper leaf disease recognition in a human palm background

The identification of pepper leaf diseases is crucial for ensuring the safety and quality of pepper yield. However, existing methods heavily rely on manual diagnosis, resulting in inefficiencies and inaccuracies. In this study, we propose a lightweight convolutional neural network (CNN) model for recognizing pepper leaf diseases and subsequently develop an application based on this model. To begin with, we acquired various images depicting healthy leaves as well as leaves affected by viral diseases, brown spots, and leaf mold. It is noteworthy that these images were captured against a background of human palms, which is commonly encountered in field conditions. The proposed CNN model adopts the GGM-VGG16 architecture, incorporating Ghost modules, global average pooling, and multi-scale convolution. Following training with the collected image dataset, the model was deployed on a mobile terminal, where an application for pepper leaf disease recognition was developed using Android Studio. Experimental results indicate that the proposed model achieved 100 % accuracy on images with a human palm background, while also demonstrating satisfactory performance on images with other backgrounds, achieving an accuracy of 87.38 %. Furthermore, the developed application has a compact size of only 12.84 MB and exhibits robust performance in recognizing pepper leaf diseases.

FER-BHARAT: a lightweight deep learning network for efficient unimodal facial emotion recognition in Indian context

Humans' ability to manage their emotions has a big impact on their ability to plan and make decisions. In order to better understand people and improve human–machine interaction, researchers in affective computing and artificial intelligence are investigating the detection and recognition of emotions. However, different cultures have distinct ways of expressing emotions, and the existing emotion recognition datasets and models may not effectively capture the nuances of the Indian population. To address this gap, this study proposes custom-built lightweight Convolutional Neural Network (CNN) models that are optimized for accuracy and computational efficiency. These models are trained and evaluated on two Indian emotion datasets: The Indian Spontaneous Expression Dataset (ISED) and the Indian Semi Acted Facial Expression Database (iSAFE). The proposed CNN model with manual feature extraction provides remarkable accuracy improvement of 11.14% for ISED and 4.72% for iSAFE datasets as compared to baseline, while reducing the training time. The proposed model also surpasses the accuracy produced by pre-trained ResNet-50 model by 0.27% ISED and by 0.24% for the iSAFE dataset with significant improvement in training time of approximately 320 s for ISED and 60 s for iSAFE dataset. The suggested lightweight CNN model with manual feature extraction offers the advantage of being computationally efficient and more accurate compared to pre-trained model making it a more practical and efficient solution for emotion recognition among Indians.

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model.

In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.

A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals

Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method.

LSGNet: A lightweight convolutional neural network model for tomato disease identification

Tomatoes are among the most extensively grown and consumed crops worldwide, but tomato production can be greatly reduced due to various diseases. Plant diseases show different symptoms at different stages. In addition, there are similarities in the symptoms of different types of plant diseases, which hinder the recognition of diseases by existing deep learning models. Traditional convolutional neural network (CNN) models for disease recognition have a large number of parameters that require high computational resources. To overcome these challenges, we propose a lightweight CNN model named LSGNet (lightweight sandglass network) for tomato disease identification. The LSGNet backbone consists of the sandglass with efficient channel attention (SGECA) and the position aware circular convolution sandglass (ParcSG) modules. The SGECA module reduces the interference of complex environments and thus focuses on extracting useful feature information. The ParcSG module has a global receptive field, which provides more detailed feature information on disease recognition. The results show that the recognition accuracies are 92.37%, 94.32%, 89.64%, 92.70%, 94.43%, 90.97%, 89.42%, 92.98%, 89.58%, and 95.54% for AlexNet, ResNet50, VGG16, MobileNetV3-Large, ShuffleNetV2-1 × , EfficientNetV2-Small, ViT-Base, MobileViT-Small, Swin-Tiny, and LSGNet. Therefore, LSGNet has higher accuracy in recognizing tomato diseases compared to other classical models. In addition, LSGNet uses 0.75 million parameters. Compared to the lightweight CNN model MobileNetV3-Large, it only has 18% of the parameters. As a whole, the advantages of LSGNet in efficiency and lightweight structure would make it a useful tool for tomato disease recognition on mobile or embedded devices.

Operation and Productivity Monitoring from Sound Signal of Legacy Pipe Bending Machine via Convolutional Neural Network (CNN)

This study introduces a non-invasive approach to monitor operation and productivity of a legacy pipe bending machine in real-time based on a lightweight convolutional neural network (CNN) model and internal sound as input data. Various sensors were deployed to determine the optimal sensor type and placement, and labels for training and testing the CNN model were generated through the meticulous collection of sound data in conjunction with webcam videos. The CNN model, which was optimized through hyperparameter tuning via grid search and utilized feature extraction using Log-Mel spectrogram, demonstrated notable prediction accuracies in the test. However, when applied in a real-world manufacturing scenario, the model encountered a significant number of errors in predicting productivity. To navigate through this challenge and enhance the predictive accuracy of the system, a buffer algorithm using the inferences of CNN models was proposed. This algorithm employs a queuing method for continuous sound monitoring securing robust predictions, refines the interpretation of the CNN model inferences, and enhances prediction outcomes in actual implementation where accuracy of monitoring productivity information is crucial. The proposed lightweight CNN model alongside the buffer algorithm was successfully deployed on an edge computer, enabling real-time remote monitoring.

Lightweight tensorial convolutional neural network for lunar impact crater detection

Impact crater detection is one of the most important and critical studies of the geological history of planetary surfaces. Recent literature on crater identification demonstrates the powerful potential of deep-learning algorithms. However, these methods potentially assumed that the detection is conducted in devices with sufficient computing resources, and used networks with massive learnable parameters to obtain superb performance. In this paper, we proposed a lightweight tensorial convolutional neural network (CNN) for lunar impact crater detection. Specifically, we develop a tensorial you only look once (TYOLO) network by representing the convolutional kernels in tensor train (TT) decomposition. Thus, the parameter redundancy of TYOLO can be greatly reduced. To the best of our knowledge, this is the first work that considers the lightweight CNN model for impact crater detection. We demonstrate the lunar impact crater detection results over the ChangE-2 lunar digital orthophotos model (DOM) and digital evaluated model (DEM). Experimental results show that the proposed TYOLO network can obtain 1.44×speed up and 39× compression with 2.4% improvement in crater detection. Overall, the proposed network demonstrates great potential in spacecraft with emerging computing resource constraints.

Pattern Augmented Lightweight Convolutional Neural Network for Intrusion Detection System

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches. Most proposed works in this research field aim to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns by utilizing the Short-Term Fourier transform (STFT). The resulting high-quality image dataset allowed us to devise an anomaly detection system for IDS using a simple lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSLKDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have demonstrated the efficacy of our raw dataset to image dataset conversion methodology, which is evident as an excellent generalization of the proposed lightweight CNN model was observed, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed versions.

Lightweight convolutional neural network (CNN) model for obesity early detection using thermal images.

The presence of a lightweight convolutional neural network (CNN) model with a high-accuracy rate and low complexity can be useful in building an early obesity detection system, especially on mobile-based applications. The previous works of the CNN model for obesity detection were focused on the accuracy performances without considering the complexity size. In this study, we aim to build a new lightweight CNN model that can accurately classify normal and obese thermograms with low complexity sizes. The DenseNet201 CNN architectures were modified by replacing the standard convolution layers with multiple depthwise and pointwise convolution layers from the MobileNet architectures. Then, the depth network of the dense block was reduced to determine which depths were the most comparable to obtain minimum validation losses. The proposed model then was compared with state-of-the-art DenseNet and MobileNet CNN models in terms of classification performances, and complexity size, which is measured in model size and computation cost. The results of the testing experiment show that the proposed model has achieved an accuracy of 81.54% with a model size of 1.44 megabyte (MB). This accuracy was comparable to that of DenseNet, which was 83.08%. However, DenseNet's model size was 71.77 MB. On the other hand, the proposed model's accuracy was higher than that of MobileNetV2, which was 79.23%, with a computation cost of 0.69 billion floating-point operations per second (GFLOPS), which approximated that of MobileNetV2, which was 0.59 GFLOPS. The proposed model inherited the feature-extracting ability from the DenseNet201 architecture while keeping the lightweight complexity characteristic of the MobileNet architecture.

A Real Time Face Recognition and Tracking Framework Using Lightweight Convolutional Neural Network

Human face recognition and tracking (FRT) plays a vital role in various fields, including security, authentication, and human-computer interaction. The main modules of the FRT system are detection, feature extraction, and FRT. Using a database, these units recognize faces as well as their location, movement, and visible features. The framework aims to process large visual data in real-time, enabling accurate and fast FRT. The paper develops a real-time FRT framework using a lightweight CNN convolutional neural network to accurately match images of faces and environments with different illumination and expression differences to improve performance. This paper focused on the development of real-time facial recognition and tracking systems. The model used to achieve this is based on deep learning (DL) using a lightweight convolutional neural network (CNN) and post-feature extraction using linear discriminant analysis (LDA). Histogram of Oriented Gradients (HOG) experiments demonstrate that DL with lightweight CNN models provides a good solution for FRT tasks, even in challenging situations including changes in position, expression, illumination, and occlusion. The results of CNN-based DL were compared with several experiments. The model was also compared with many modern methods and achieved better results. The lightweight CNN model for DL outperformed it 100% of the time. When the split rate is 70:30 and the learning rate is 0.001, the epoch is 100. This demonstrates the dominance of DL over other techniques and shows how well it handles FRT tasks using lightweight and even real-time CNN methods.

Underwater Engineering Crack Identification based on Lightweight Convolutional Neural Network

Convolutional neural network is an effective model for image feature recognition, which is widely used in crack recognition and risk detection. The crack identification of underwater engineering involves a huge scale of data, and the traditional R-CNN model and Faster R-CNN model have the problems of large number of model parameters and low efficiency for crack identification. Therefore, a lightweight convolutional neural network based crack identification method for underwater engineering is proposed to optimize the efficiency and accuracy of crack identification. Deep separable convolution is used to replace the common convolution of YOLOX network (an open source high-performance object detection algorithm), reducing the redundant parameters of the model to achieve lightweight design. Following the backbone feature extraction network framework of YOLOX, Transformer (a structure based on attention mechanism and forward neural network) vision module is adopted to replace the CSP structure (a feature extraction structure) at the end of the backbone network, adding attention mechanism and enhancing key information extraction capability. Make up for the problem of decreasing feature extraction accuracy caused by decreasing parameter number; Finally, the adaptive spatial feature fusion strategy is used instead of the traditional feature pyramid cascade to implement feature fusion and improve the feature extraction ability of small targets. The test results show that the improved YOLOX lightweight convolutional neural network has good prediction ability for underwater engineering cracks, and can accurately select the crack location, with fewer false detection and missed detection cases. Ablation experiments show that this study builds a lightweight convolutional neural network model, which improves the accuracy of the model to identify underwater engineering cracks. The improved lightweight convolutional neural network is suitable for crack identification in underwater engineering and can be widely applied in the field of underwater engineering risk identification.

LMFRNet: A Lightweight Convolutional Neural Network Model for Image Analysis

Convolutional neural networks (CNNs) have transformed the landscape of image analysis and are widely applied across various fields. With their widespread adoption in fields like medical diagnosis and autonomous driving, CNNs have demonstrated powerful capabilities. Despite their success, existing models face challenges in deploying and operating in resource-constrained environments, limiting their practicality in real-world scenarios. We introduce LMFRNet, a lightweight CNN model. Its innovation resides in a multi-feature block design, effectively reducing both model complexity and computational load. Achieving an exceptional accuracy of 94.6% on the CIFAR-10 dataset, this model showcases remarkable performance while demonstrating parsimonious resource utilization. We further validate the performance of the model on the CIFAR-100, MNIST, and Fashion-MNIST datasets, demonstrating its robustness and generalizability across diverse datasets. Furthermore, we conducted extensive experiments to investigate the influence of critical hyperparameters. These experiments provided valuable insights for effective model training.

Ensemble of transfer learning and light-weight convolutional neural network model for an effective ear recognition system

Ensemble of transfer learning and light-weight convolutional neural network model for an effective ear recognition system

An EEG-based marker of functional connectivity: detection of major depressive disorder.

Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains a pressing challenge. In this study, we present a fusion feature called P-MSWC, as a novel marker to construct brain functional connectivity matrices and utilize the convolutional neural network (CNN) to identify MDD based on electroencephalogram (EEG) signal. Firstly, we combine synchrosqueezed wavelet transform and coherence theory to get synchrosqueezed wavelet coherence. Then, we obtain the fusion feature by incorporating synchrosqueezed wavelet coherence value and phase-locking value, which outperforms conventional functional connectivity markers by comprehensively capturing the original EEG signal's information and demonstrating notable noise-resistance capabilities. Finally, we propose a lightweight CNN model that effectively utilizes the high-dimensional connectivity matrix of the brain, constructed using our novel marker, to enable more accurate and efficient detection of MDD. The proposed method achieves 99.92% accuracy on a single dataset and 97.86% accuracy on a combined dataset. Moreover, comparison experiments have shown that the performance of the proposed method is superior to traditional machine learning methods. Furthermore, visualization experiments reveal differences in the distribution of brain connectivity between MDD patients and healthy subjects, including decreased connectivity in the T7, O1, F8, and C3 channels of the gamma band. The results of the experiments indicate that the fusion feature can be utilized as a new marker for constructing functional brain connectivity, and the combination of deep learning and functional connectivity matrices can provide more help for the detection of MDD.

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

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.