Histogram Of Oriented Gradients Research Articles

A HOG Feature Extractor and KNN-Based Method for Underwater Image Classification

Underwater garbage affects the life of marine creatures and the entire ecosystem. Detecting underwater garbage is an important research area. In this study, a method is proposed to detect underwater garbage. The open-access Trash-ICRA19 dataset was used to implement the proposed method. The data set cropping process was applied and a data set consisting of 11060 images in total was obtained. These images were converted to 200×200 pixels using preprocessing. By applying the Directed Gradient Histogram (HOG) algorithm, 11060×900 feature vectors were obtained. The resulting feature vectors were then calculated using KNN (K Nearest Neighbor Algorithm), DT (Decision Tree), LD (Linear Discriminant), NB (Naive Bayes), and SVM (Support Vector Machine) classifiers. The results obtained showed that 97.78% accuracy was obtained when the KNN classifier was used in this method. The use of only feature extractors and classifiers in the proposed method shows that the method is lightweight. It has low computational complexity compared to existing studies in the literature. Moreover, according to its performance results, it is more successful than the methods in the literature.

Selective Partitioned Regression for Accurate Kidney Health Monitoring

The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.

HBMD-Net: Feature Fusion Based Breast Cancer Classification with Class Imbalance Resolution.

Breast cancer, a widespread global disease, represents a significant threat to women's health and lives, ranking as one of the most vulnerable malignant tumors they face. Many researchers have proposed their computer-aided diagnosis systems for classifying breast cancer. The majority of these approaches primarily utilize deep learning (DL) methods, which are not entirely reliable. These approaches overlook the crucial necessity of incorporating both local and global information for precise tumor detection, despite the fact that the subtle nuances are crucial for precise breast cancer classification. In addition, there are a limited number of publicly available breast cancer datasets, and the ones that are available tend to be imbalanced in nature. Therefore, this paper presents the hybrid breast mass detection-network (HBMD-Net) to address two critical challenges: class imbalance and the need to recognize that relying solely on either global or local features falls short in achieving precise tumor classification. To overcome the problem of class imbalance, HBMD-Net incorporates the borderline synthetic minority over-sampling technique (BSMOTE). Simultaneously, it employs a feature fusion approach, combining features by utilizing ResNet50 to extract deep features that provide global information, while handcrafted features are derived using histogram orientation gradient (HOG), that provide local information. In addition, an ROI segmentation has been implemented to avoid misclassifications. This integrated strategy substantially enhances breast cancer classification performance. Moreover, the proposed method integrates the block matching and 3D (BM3D) denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The evaluation of the proposed HBMD-Net encompasses two breast ultrasound (BUS) datasets, namely BUSI and UDIAT. The proposed model has demonstrated a satisfactory performance, achieving accuracies of 99.14% and 94.49% respectively.

Qin Seal Script Character Recognition with Fuzzy and Incomplete Information

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.

A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images

COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate. Owing to the COVID-19 pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging is among the most effective techniques of respiratory disorders detection through machine learning and deep learning. However, conventional machine learning methods depend on extracted and engineered features, whereby the optimum features influence the classifier's performance. In this study, Histogram of Oriented Gradient (HOG) and eight deep learning models were utilized for feature extraction while K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) were used for classification. A combined feature of HOG and deep learning feature was proposed to improve the performance of the classifiers. VGG-16 + HOG achieved 99.4 overall accuracy with SVM. This indicates that our proposed concatenated feature can enhance the SVM classifier's performance in COVID-19 detection.

A multiscale carotid plaque detection method based on two-stage analysis

To develop a method for accurate identification of multiscale carotid plaques in ultrasound images. We proposed a two-stage carotid plaque detection method based on deep convolutional neural network (SM-YOLO).A series of algorithms such as median filtering, histogram equalization, and Gamma transformation were used to preprocess the dataset to improve image quality. In the first stage of the model construction, a candidate plaque set was built based on the YOLOX_l target detection network, using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes. In the second stage, the Histogram of Oriented Gradient (HOG) features and Local Binary Pattern (LBP) features were extracted and fused, and a Support Vector Machine (SVM) classifier was used to screen the candidate plaque set to obtain the final detection results. This model was compared quantitatively and visually with several target detection models (YOLOX_l, SSD, EfficientDet, YOLOV5_l, Faster R-CNN). SM-YOLO achieved a recall of 89.44%, an accuracy of 90.96%, a F1-Score of 90.19%, and an AP of 92.70% on the test set, outperforming other models in all performance indicators and visual effects. The constructed model had a much shorter detection time than the Faster R-CNN model (only one third of that of the latter), thus meeting the requirements of real-time detection. The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

AbstractThe field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation

Deep neural networks are effectively utilized for the instance segmentation of muck images from tunnel boring machines (TBMs), providing real-time insights into the surrounding rock condition. However, the high cost of obtaining quality labeled data limits the widespread application of this method. Addressing this challenge, this study presents a semi-symmetrical, fully convolutional masked autoencoder designed for self-supervised pre-training on extensive unlabeled muck image datasets. The model features a four-tier sparse encoder for down-sampling and a two-tier sparse decoder for up-sampling, connected via a conventional convolutional neck, forming a semi-symmetrical structure. This design enhances the model’s ability to capture essential low-level features, including geometric shapes and object boundaries. Additionally, to circumvent the trivial solutions in pixel regression that the original masked autoencoder faced, Histogram of Oriented Gradients (HOG) descriptors and Laplacian features have been integrated as novel self-supervision targets. Testing shows that the proposed model can effectively discern essential features of muck images in self-supervised training. When applied to subsequent end-to-end training tasks, it enhances the model’s performance, increasing the prediction accuracy of Intersection over Union (IoU) for muck boundaries and regions by 5.9% and 2.4%, respectively, outperforming the enhancements made by the original masked autoencoder.

Hand Gesture Recognition Using AI/ML

Abstract: Every day, we see many people who are facing illnesses as deaf, dumb and blind etc. They face difficulties in interacting with others. Previously developed techniques were all sensors based and they didn’t give a general solution. This article explains a new technology for virtual conversations without sensors. An image processing technique called Histogram of gradient (HOG) along with an artificial neural network (ANN) has been used to train the system. The Web Camera is used to take an image of different gestures and that will be used as input to the Mat lab. The software will recognize the image and identify the core speech output to be processed using the speech replay suite. Sign language is composed of continuous gestures, therefore, for sign language recognition, in addition to spatial domain, it also needs to capture motion information across multiple consecutive video frames.This paper explains two way communications between the deaf, dumb and normal people which means the proposed system is capable of converting the sign language to text and voice.

The Utilization and Optimization of Histogram of Oriented Gradients and Machine Learning in Face Recognition System

Computer science and technology development in recent years has experienced great developments. This time, some types of technology digitise almost everything related to human life, including facial recognition. In recent years, various methods for recognising human faces have developed. One of them is using the Histogram of Oriented Gradients (HOG). On this occasion, an image processing system will be designed to recognise human faces using Histograms of Oriented Gradients (HOG) and machine learning such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). Detects the winking of the face, using computer-recognisable points in the eye area from 68 facial landmarks, so from these results, the distance between the upper and lower eyelids can be measured. If the distance (in pixels) is small enough, it can be interpreted as a wink. In addition, it is also limited by the distance of faces that can be detected to blink. In the end, if a recognised face blinks are detected, the time and date will be recorded. It will then open a solenoid lock using serial communication via Arduino Uno to become a security system. From 100 facial photos and 207 blink tests, 89.86% found that the computer could detect a "True Positive" wink. Besides, this facial recognition system's recommended tolerance parameter value is between 0.42 and 0.48.

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

Sports Video Classification Method Based on Improved Deep Learning

Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance video quality. The model then uses a bifurcated neural network to extract detailed features, leading to a densely connected neural network with a specific activation function for categorizing videos. We tested our model on a High-Definition Sports Video Dataset covering over 20 sports and a low-resolution dataset. Our model outperformed established classifiers like DenseNet, VggNet, Inception v3, and ResNet-50. It achieved high precision (0.9718), accuracy (0.9804), F-score (0.9761), and recall (0.9723) on the high-resolution dataset, and significantly better precision (0.8725) on the low-resolution dataset. Correspondingly, the highest values on the matrix of four traditional models are: precision (0.9690), accuracy (0.9781), F-score (0.9670), recall (0.9681) on the high-resolution dataset, and precision (0.8627) on the low-resolution dataset. This demonstrates our model’s superior performance in sports video classification under various conditions, including rapid motion and low resolution. It marks a significant step forward in sports data analytics and content categorization.

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

This paper presents a novel approach for emotion recognition (ER) based on Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), and computer vision. The proposed system includes two different models for physiological signals and facial expressions deployed in a real-time embedded system. A custom dataset for EEG, ECG, EMG, and facial expression was collected from 10 participants using an Affective Video Response System. Time, frequency, and wavelet domain-specific features were extracted and optimized, based on their Visualizations from Exploratory Data Analysis (EDA) and Principal Component Analysis (PCA). Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Histogram of Oriented Gradients (HOG), and Gabor descriptors were used for differentiating facial emotions. Classification models, namely decision tree, random forest, and optimized variants thereof, were trained using these features. The optimized Random Forest model achieved an accuracy of 84%, while the optimized Decision Tree achieved 76% for the physiological signal-based model. The facial emotion recognition (FER) model attained an accuracy of 84.6%, 74.3%, 67%, and 64.5% using K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and XGBoost, respectively. Performance metrics, including Area Under Curve (AUC), F1 score, and Receiver Operating Characteristic Curve (ROC), were computed to evaluate the models. The outcome of both results, i.e., the fusion of bio-signals and facial emotion analysis, is given to a voting classifier to get the final emotion. A comprehensive report is generated using the Generative Pretrained Transformer (GPT) language model based on the resultant emotion, achieving an accuracy of 87.5%. The model was implemented and deployed on a Jetson Nano. The results show its relevance to ER. It has applications in enhancing prosthetic systems and other medical fields such as psychological therapy, rehabilitation, assisting individuals with neurological disorders, mental health monitoring, and biometric security.

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.

Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition.

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine.

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

Study of the fault diagnosis method for gas turbine sensors based on inter-parameter coupling information

Correct and reliable measurement data are crucial for state monitoring, safe operations, health assessment, and life prediction of integrated energy systems (IESs). Sensors are often installed in harsh environments and prone to all kinds of faults; therefore, it is necessary to diagnose sensor faults. A diagnostic method for sensor faults based on gradient histogram distribution (GHD) combined with light gradient boosting machine (LightGBM) is presented in this paper. This proposed method effectively utilizes the coupling information between the relevant parameters. The GHD efficiently extracted the time-domain characteristics of sensor faults and reduced the dimension of eigenvectors. This is beneficial to increasing the diagnostic speed. The kernel density estimation distributions of the gradient and eigenvectors for the sensor with strong correlation are similar, but that for the sensor with weak correlation are completely different. A LightGBM classifier trained based on the feature vectors was utilized to diagnose and classify the sensor faults. The diagnosis accuracy and the diagnosis time of this developed method were examined using the multiple-condition practical operation data of gas turbines in the IES. The experiment results demonstrate that the diagnostic accuracy of five sensor faults using this developed method is all above 90%. The diagnostic time is about 0.47–1.34 s, and is less than 2 s for the gradual faults.

A Deep Learning Approach to Discover Router Firmware Vulnerabilities

An attack on Industrial IoT systems can cause severe damage to connected devices and their owners. Therefore, detecting router firmware vulnerabilities has become a critical issue. However, collecting a dataset of firmware samples is challenging as no open-source datasets are available online. A manual effort was required to verify the states of samples in both the Common Vulnerabilities and Exposures (CVE) and The National Vulnerability Database (NVD) databases as either vulnerable or benign. After verification, 1450 samples were collected. This paper investigates the effectiveness of using convolutional neural networks (CNNs) and computer vision techniques to analyze home router firmware. The collected firmware samples were read as an array of byte strings, divided into sub-arrays based on the image's dimensions, then layered on top of one another to produce the firmware images. The images were divided by manufacturer and used as inputs for various CNN models to test their accuracy. Three statistical filtering algorithms were used on each manufacturer's set to produce multiple versions of each set, totaling 24 datasets across four manufacturers, with six datasets per manufacturer (4 filtered images and two grayscale and RGB images). The image filter algorithms used include local binary pattern (LBP), histogram-oriented gradients (HOG), and Gabor filter used on the LBP and HOG sets. After testing all combinations of the filtered/normal datasets with the CNN training model, the HOG filter was the most accurate, with an average accuracy of 85.81% across all tests and models, with results as high as 97.94% when used with the appropriate CNN model.

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.

Employing combined spatial and frequency domain image features for machine learning-based malware detection

<p>The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD.</p>