Histogram Of Oriented Gradients Research Articles

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>

Neutrosophic Fuzzy Logic based SVM approach for Enhanced Skin Cancer Prediction

In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing.

The Value and Use of Chinese Traditional Music in Dance Performance in the Internet Era

Abstract In this study, Gaussian mixture model preprocessing and histogram of orientation gradient (HOG) and histogram of optical flow direction (HOF) action feature extraction techniques are used to significantly optimize the accuracy of dance performance action recognition. In addition, the VNect pose estimation algorithm and dual-stream 3D-CNN-based skeleton action recognition are used to improve significantly the accuracy and efficiency of recognition. In terms of music feature recognition, this study utilizes short-time time domain and frequency domain analysis techniques and BP neural networks to effectively achieve accurate recognition and sentiment classification of music features. Analyzing a dataset containing 6000 training videos, 800 validation videos, and 1200 test videos, the study reveals that musical elements, melody, rhythm, emotional tone, and song style significantly impact the stage performance effect. The study not only highlights the importance of traditional Chinese music in dance performance to enhance the artistic effect and deepen the emotional expression, but also opens up new perspectives and methods for disseminating and developing traditional culture.

A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion

The increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric systems. These systems combine several biometric characteristics and sources by using fusion methods. Through score-level fusion, this work integrates facial and iris recognition techniques to present a multimodal biometric recognition methodology. The Histogram of Oriented Gradients (HOG) descriptor is used in the facial recognition system to extract facial characteristics, while the deep Wavelet Scattering Transform Network (WSTN) is applied in the iris recognition system to extract iris features. Then, for customized recognition classification, the feature vectors from every facial and iris recognition system are fed into a multiclass logistic regression. These systems provide scores, which are then combined via score-level fusion to maximize the efficiency of the human recognition process. The realistic multimodal database known as (MULB) is used to assess the suggested system's performance. The suggested technique exhibits improved performance across several measures, such as precision, recall, accuracy, equal error rate, false acceptance rate, and false rejection rate, as demonstrated by the experimental findings. The face and iris biometric systems have individual accuracy rates of 96.45% and 95.31% respectively. The equal error rates for the face and iris are 1.79% and 2.36% respectively. Simultaneously, the proposed multimodal biometric system attains a markedly enhanced accuracy rate of 100% and an equal error rate as little as 0.26%.

Convex-based lightweight feature descriptor for Augmented Reality Tracking.

Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD's recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.

Lip Movement Recognition using Histogram of Oriented Gradient (HOG) and Support Machine Vector (SVM) for Arabic Word

This research aims to develop a lip gesture recognition system in Arabic words by utilizing Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) classification. The evaluation was conducted on a dataset of 1749 videos with male and female participation using Modern Standard Arabic. The 10 cross-fold validation method was used to measure the performance of the system. By applying a polynomial kernel, this study achieved an accuracy rate of 95.63%, while the word recognition rate reached 96%. These results confirm the system's ability to recognize lip movements with precision, confirming the effectiveness of the approach used in visual recognition for Arabic.

Construction and Evaluation of College English Translation Teaching Model Based on Multimodal Integration

Abstract Amidst the swift advancements in multimedia technology and artificial intelligence, the exploration and deployment of intelligent educational technologies that leverage deep learning and neural networks have intensified. This study introduces an innovative automatic student behavior recognition model tailored for college English translation classrooms. Utilizing Multimodal Fusion, the model employs a Histogram of Oriented Gradients (HOG) to capture the facial expression modality and Mel Frequency Cepstral Coefficients (MFCCs) to discern the linguistic modality of students. Through Linear Discriminant Analysis, the model effectively reduces the dimensionality of multimodal data, facilitating the integration of both modalities for enhanced feature fusion and decision-making processes. Following the acquisition of multimodal emotion recognition data, students evaluate the recognized emotion values, enabling the calibration of an emotion scale. This scale serves to align specific learning emotions with corresponding learning states, providing a framework to assess students’ engagement levels. A notable improvement was observed over four weeks, with the average incidence of students classified under a “positive” learning state escalating from 9.5 to 26.7. This significant rise underscores the increasing engagement and interaction within the college English translation classroom, facilitated by the Multimodal Fusion model. It illustrates the enhanced convenience for educators in managing classroom dynamics.

Sign Language Master Using AI

We witness many people who face disabilities like being deaf, dumb, blind etc. They face a lot of challenges and difficulties trying to interact and communicate with others. This paper presents a new technique by providing a virtual solution without making use of any sensors. Histogram Oriented Gradient (HOG) along with Convolutional Neural Network (CNN) have been implemented. The algorithm recognizes the real-time image gestures, identifies the text, and gives voice output. In this paper, we introduce a Sign Language recognition. The user must be able to capture images of hand gestures using a web camera in this analysis, and the system must predict and show the name of the captured image. The captured image undergoes series of processing steps, which include various Computer vision techniques such as the conversion to gray - scale, and mask operation. Convolutional Neural Network (CNN) is used to train our model and identify the pictures.

Disease Prognosis of Fetal Heart’s Four-Chamber and Blood Vessels in Ultrasound Images Using CNN Incorporated VGG 16 and Enhanced DRNN

Fetal Heart Disease (FHD) based Structural Heart Disorders (SHD) occur when certain features of the heart develop abnormally. These flaws may cause blood flow to circulate in the erroneous spot, slow down, or be utterly blocked. Heart Defects (HD) caused via FHD or disorders that primarily impact embryonic heart conditions are alternatively referred to as Congenital Heart Defects (CHD). Multiple prior investigation algorithms such as Multi-Resolution Convolutional Neural Network (MRCNN), Deep Convolutional Neural Network (DCNN), Faster-RCNN (FRCNN) and DANomaly Wgan-GP and Convolutional Neural Network (DGACNN) rendered in the detection of FHD. Yet, the models have endured several challenges due to fuzzy constraints and irrelevant adherence. The intended aim is to detect the dilemma of the fetal heart in UltraSound (US) images using two distinct tier methods. The initial tier detects the fetal heart chamber's walls and valves using the Convolutional Neural Network (CNN)-incorporated Visual Geometry Group 16 (VGG 16) technique for processing fetal ultrasound images, allowing it to detect and dissolve anomalies in heart walls. This initial investigation concerns improving the image's quality in each subsequent sequence, from lowest to most improved using the conventional Augmented Wiener Filtering (AWF) approach. Succeeding, an instance-level Region of Interest (ROI) segmentation for exploiting the feature mining approach will be carried out via spatial features masking and ground-truth labeling framework for septal defect diagnosis. The second tier determines the flaws in fetal heart blood flow size, structure and vessels utilizing Deep Recurrent Neural Network (DRNN) integrated with region-based texture characteristics Local-Binary-Pattern (LBP), Histogram-of-Oriented-Gradient (HOG) and the Bags Of Features (BOF) segmentation framework via image acquiring. In eventual, the histogram equalization enhancement algorithm with Median Modified Wiener Filter (MMWF) is enumerated to enhance the visual quality, tests for signal-to-noise ratio, rate of variations, and noise proportion for sorting the blood vessels of the input fetal image. The analyzed CNN’s VGG 16 and DRNN model’s efficiency via Matrix Laboratory (MATLAB) has detected the cardiac features both in normal and abnormal ranges with an overall accuracy of 99.89% and 98.7%.