Mask Regional Convolutional Neural Network Research Articles

Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

AbstractUnderwater imaging enables nondestructive plankton sampling at frequencies, durations, and resolutions unattainable by traditional methods. These systems necessitate automated processes to identify organisms efficiently. Early underwater image processing used a standard approach: binarizing images to segment targets, then integrating deep learning models for classification. While intuitive, this infrastructure has limitations in handling high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and highly variable target sizes. To address these challenges, we introduce a new framework that starts with a scene classifier to capture large within‐image variation, such as disparities in the layout of particles and dominant taxa. After scene classification, scene‐specific Mask regional convolutional neural network (Mask R‐CNN) models are trained to separate target objects into different groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal plankton images, we compared the scene‐specific models to the Mask R‐CNN model encompassing all scene categories as a single full model. Results showed that the scene‐specific approach outperformed the full model by achieving a 20% accuracy improvement in complex noisy images. The full model yielded counts that were up to 78% lower than those enumerated by the scene‐specific model for some small‐sized plankton groups. We further tested the framework on images from a benthic video camera and an imaging sonar system with good results. The integration of scene classification, which groups similar images together, can improve the accuracy of detection and classification for complex marine biological images.

Automated estimation of stomatal number and aperture in haskap (Lonicera caerulea L.).

This study developed the reliable Mask R-CNN model to detect stomata in Lonicera caerulea. The obtained data could be utilized for evaluating some characters such as stomatal number and aperture area. The native distribution of haskap (Lonicera caerulea L.), a small-shrub species, extends through Northern Eurasia, Japan, and North America. Stomatal observation is important for plant research to evaluate the physiological status and to investigate the effect of ploidy levels on phenotypes. However, manual annotation of stomata using microscope software or ImageJ is time consuming. Therefore, an efficient method to phenotype stomata is needed. In this study, we used the Mask Regional Convolutional Neural Network (Mask R-CNN), a deep learning model, to analyze the stomata of haskap efficiently and accurately. We analyzed haskap plants (dwarf and giant phenotypes) with the same ploidy but different phenotypes, including leaf area, stomatal aperture area, stomatal density, and total number of stomata. The R-square value of the estimated stomatal aperture area was 0.92 and 0.93 for the dwarf and giant plants, respectively. The R-square value of the estimated stomatal number was 0.99 and 0.98 for the two phenotypes. The results showed that the measurements obtained using the models were as accurate as the manual measurements. Statistical analysis revealed that the stomatal density of the dwarf plants was higher than that of the giant plants, but the maximum stomatal aperture area, average stomatal aperture area, total number of stomata, and average leaf area were lower than those of the giant plants. A high-precision, rapid, and large-scale detection method was developed by training the Mask R-CNN model. This model can help save time and increase the volume of data.

Loess landslides detection via a partially supervised learning and improved Mask-RCNN with multi-source remote sensing data

Loess landslides pose a severe threat of destruction, and detecting them is crucial for minimizing their impact on society. They typically consist of wind-deposited clay and silt, which makes them challenging to detect using conventional methods. Techniques like visual interpretation and field surveys are the most useful, yet these methods can be laborious, expensive, and require a certain level of prior knowledge. Furthermore, remote sensing approaches face the challenge of distinguishing between natural erosion and landslides. Recently, deep learning for landslide detection has the potential to speed up and improve detection accuracy. Nonetheless, deep learning models require large amounts of labeled data and the development of robust algorithms capable of extracting meaningful features from remote sensing data. In this article, a novel approach is introduced to improve the Mask Regional Convolutional Neural Network (Mask-RCNN) algorithm for accurately detecting landslides when the number of available segmentation mask samples is limited. Specifically, A novel loess landslides dataset is established using high-resolution remote sensing images in Gansu Province. In the context of partially supervised learning, a neural network branch containing a weight transfer function is developed to capture mask information from bounding boxes. Additionally, a mask-scoring network block is used to learn the quality of predicted instance masks. Our modified algorithm achieves an average precision improvement of 20.7% compared to the original Mask R-CNN algorithm in small landslide detection. The mask IoU threshold value of 0.5 is used to estimate the average accuracy higher than 0.75. The average precision of the segmentation mask is improved by 16.7% in test set. By proposing a solution that can achieve accurate landslide detection while using limited labeled samples, this study makes a valuable contribution to the application of deep learning in the domains of remote sensing and landslide detection.

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice.

Automatic fracture detection from the images of electrical image logs using Mask R-CNN

This paper presents a novel approach for automatically detecting fractures from electrical image logs using a state-of-the-art object detection and segmentation method, i.e., the Mask Regional Convolutional Neural Network (Mask R-CNN). First, we apply image processing algorithms of inpainting and augmentation to generate the basic dataset. The fractures in those processed images are properly identified and labeled. The processed images with the annotated fractures will be used as input data for the training and testing of the Mask R-CNN model. Subsequently, the input data are randomly split into training dataset, validation dataset, and testing dataset. The Mask R-CNN model is trained using the training and validation datasets. The performance and accuracy of the trained model is tested by comparing the predicted fractures from the testing dataset to the actual fractures that are identified manually. The testing results demonstrate that the trained Mask R-CNN model has precision and recall values of 96% and 92% in detecting fractures from the image logs. Last, we further validate the proposed approach by comparing the fractures detected using the developed Mask R-CNN model against those identified from three core samples. The comparison also indicates a high accuracy of the Mask R-CNN model.

Automated Fish Detection and Tracking System Using Pre-Trained Mask R-CNN for Ecological Biodiversity

Introduce a new dynamic classifying algorithm in this paper to recognize and monitor fish activity to simultaneously better comprehend their synapomorphies. The pre-trained Mask Regional Convolutional Neural Network (Mask-R-CNN) is trained using a set of test models extracted from recorded video recording. The approach suggested subsequently yields well-enhanced feature vectors. The system’s automatic fish detection and tracking capabilities are improved, enhancing underwater investigation for supervising ecological biodiversity. The publicly accessible field-truth dataset assesses recall, F1-score, and classification and tracking precision. Utilizing current tracking R-CNN algorithms like Lowest Output Sum of Siamese Mask (SiamMask), Sequential Non-Maximum Suppression (Seq-NMS), and Squared Errors (MOSSE), comparative performance testing is conducted. In comparison to Siam-Mask (84%), Seq-NMS (78%) and MOSSE (75%), more than 120 of 170 specific bream were detected using the pre-trained Mask-R-CNN of the proposed algorithm (87%). This pre-trained Mask R-CNN system was used in the evaluation, and it was discovered that detection and tracking accuracy had increased significantly. This suggests that the ocean ecosystem could benefit from applying the proposed approach for management of ecology.

Materials swelling revealed through automated semantic segmentation of cavities in electron microscopy images

Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled a database of labeled cavity images which includes 400 images, > 34 k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed targeted analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications.

Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network.

Architectural distortion (AD) is a common imaging manifestation of breast cancer, but is also seen in benign lesions. This study aimed to construct deep learning models using mask regional convolutional neural network (Mask-RCNN) for AD identification in full-field digital mammography (FFDM) and evaluate the performance of models for malignant AD diagnosis. This retrospective diagnostic study was conducted at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2011 and December 2020. Patients with AD in the breast in FFDM were included. Machine learning models for AD identification were developed using the Mask RCNN method. Receiver operating characteristics (ROC) curves, their areas under the curve (AUCs), and recall/sensitivity were used to evaluate the models. Models with the highest AUCs were selected for malignant AD diagnosis. A total of 349 AD patients (190 with malignant AD) were enrolled. EfficientNetV2, EfficientNetV1, ResNext, and ResNet were developed for AD identification, with AUCs of 0.89, 0.87, 0.81 and 0.79. The AUC of EfficientNetV2 was significantly higher than EfficientNetV1 (0.89 vs. 0.78, P=0.001) for malignant AD diagnosis, and the recall/sensitivity of the EfficientNetV2 model was 0.93. The Mask-RCNN-based EfficientNetV2 model has a good diagnostic value for malignant AD.

Designing an Intelligent Lesion Detection System Using Deep Architecture Neural Networks in the Lower Limb X-Ray Images

Purpose: Diagnosis of musculoskeletal abnormalities is critical because of the large number of people affected by these disorders worldwide. The recent advances in deep learning techniques show that convolutional neural networks can be a useful tool for the computer-aided detection of radiographic abnormalities. This study focuses on diagnosing musculoskeletal abnormalities in the lower extremities using X-Ray images by deep architecture neural networks.
 Materials and Methods: The dataset contains 61,098 musculoskeletal radiographic images, including 42,658 normal and 18,440 abnormal images. Each image belongs to a single type of lower extremity radiography, including the toe, foot, ankle, leg, knee, femur, and hip joints, which were prepared with standard projection without artifacts and with high quality. A novel deep neural network architecture is proposed with two different scenarios that perform the lower extremity lesion diagnosis functions with high accuracy. The foundation of the proposed method is a deep learning framework based on the Mask Regional Convolutional Neural Network (R-CNN) and Convolutional Neural Network (CNN). The model with the best results incorporated the Mask R-CNN algorithm to produce the bounding box, followed by the CNN algorithm to detect the class based on that.
 Results: The proposed model can identify different types of lower limb lesions by an Area Under the Curve (AUC) of the Receiver Operating Characteristics (ROC) curve 0.925 with an operating point of 0.859 of sensitivity and a specificity of 0.893.
 Conclusion: The results indicated that the consecutive implementation of Mask R-CNN and CNN has a higher efficiency than Mask R-CNN and CNN separately in lesion detection of lower limb X-ray images.

Neural Network Image Segmentation for Sign Language Interpretation

The use of neural networks to recognize and classify objects in images is a popular field in computer science. It is highly likely that an object in an image chosen for classification will have a representation matrix with significantly less pixels than the background or other elements of the image. As a result, the initial plan would be to divide or segment that object from the other portions of the image that are not essential for categorization. This also serves as the study's objective, for which we employ segmentation to separate the components essential to the classification procedure and assess any room for improvement in the final classification outcome. Mask Region Convolutional Neural Network was the model used for segmentation, and Convolutional Neural Network was the model used for classification. The study's findings demonstrate a notable improvement in the classification in the case of sign language. Further advancement of image segmentation models implies better more accurate results for classification models once they are combined. Keywords— Neural network, Image segmentation, Sign language, Classification, Mask Regional Convolutional Neural Network.

Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques.

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.

Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images

The global spread of coronavirus illness has surged dramatically, resulting in a catastrophic pandemic situation. Despite this, accurate screening remains a significant challenge due to difficulties in categorizing infection regions and the minuscule difference between typical pneumonia and COVID (Coronavirus Disease) pneumonia. Diagnosing COVID-19 using the Mask Regional-Convolutional Neural Network (Mask R-CNN) is proposed to classify the chest computerized tomographic (CT) images into COVID-positive and COVID-negative. Covid-19 has a direct effect on the lungs, causing damage to the alveoli, which leads to various lung complications. By fusing multi-class data, the severity level of the patients can be classified using the meta-learning few-shot learning technique with the residual network with 50 layers deep (ResNet-50) as the base classifier. It has been tested with the outcome of COVID positive chest CT image data. From these various classes, it is possible to predict the onset possibilities of acute COVID lung disorders such as sepsis, acute respiratory distress syndrome (ARDS), COVID pneumonia, COVID bronchitis, etc. The first method of classification is proposed to diagnose whether the patient is affected by COVID-19 or not; it achieves a mean Average Precision (mAP) of 91.52% and G-mean of 97.69% with 98.60% of classification accuracy. The second method of classification is proposed for the detection of various acute lung disorders based on severity provide better performance in all the four stages, the average accuracy is of 95.4%, the G-mean for multiclass achieves 94.02%, and the AUC is 93.27% compared with the cutting-edge techniques. It enables healthcare professionals to correctly detect severity for potential treatments.

Automatic Traffic Sign Recognition System Using CNN

In recent times, self-driving vehicles have been widely adopted across different countries as they are equipped to drastically reduce the number of road accidents and congestion on the road thereby improving the traffic efficiency. To detect, identify, and label the traffic signs on the road in order to help the Advanced Driver Assistance Systems (ADAS) in these autonomous vehicles with navigation details, a Traffic Sign Recognition (TSR) System using a deep convolutional neural network model, Mask RCNN (Mask Regional Convolutional Neural Network), is proposed in this paper that aims to help the autonomous vehicles comprehend the road ahead and safely navigate to the desired destination. This paper presents the detection and labelling of Indian and European Signs and also the results of the system working efficiently under various challenging visibility conditions. The results obtained show that the Mask RCNN model has recorded higher performance compared to all the other CNN models that have been previously used for traffic sign recognition.

"Deep Learning-based Classification of Materials into Biodegradable and Non-Biodegradable"

The collection of real-time data from people, their cars, public transit, buildings, and other urban infrastructures like the energy grid and waste management systems are at the heart of the smart city concept. The insights gathered from the data may be used by municipal authorities to manage resources and services efficiently. An important study topic at the same time is the sharp increase in environmental degradation and deterioration that causes ecological imbalance. Additionally, the development of advanced waste management systems that can categorize rubbish according to its level of biodegradability is required for the worldwide expansion of smart cities. Some of the more common ones are paper, paper boxes, food, glass, and other garbage. A costeffective technique to separate the waste from the enormous mountain of trash and garbage and classify the waste items is to use computer vision-based technologies. Recent developments in deep learning (DL) and deep reinforcement learning (DRL) have made it possible to categorize waste objects and identify and detect trash. In this sense, the research creates an intelligence model for smart cities. The technique's goal is to recognize and classify rubbish objects using the DL and DRL approaches. The two steps of the SYSTEM technique are objected classification based on DRL and object detection based on Mask Regional Convolutional Neural Network (CNN)… The CNN model uses the DenseNet model as a baseline model, and a deep learning network (DLN) is employed as the classifier. A based hyperparameter optimizer is also created to boost the efficiency of the DenseNet model.

Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model.

ObjectiveMeniscus tear is a common problem in sports trauma, and its imaging diagnosis mainly relies on MRI. To improve the diagnostic accuracy and efficiency, a deep learning model was employed in this study and the identification efficiency was evaluated.MethodsStandard knee MRI images from 924 individual patients were used to complete the training, validation and testing processes. Mask regional convolutional neural network (R–CNN) was used to build the deep learning network structure, and ResNet50 was adopted to develop the backbone network. The deep learning model was trained and validated with a dataset containing 504 and 220 patients, respectively. Internal testing was performed based on a dataset of 200 patients, and 180 patients from 8 hospitals were regarded as an external dataset for model validation. Additionally, 40 patients who were diagnosed by the arthroscopic surgery were enrolled as the final test dataset.ResultsAfter training and validation, the deep learning model effectively recognized healthy and injured menisci. Average precision for the three types of menisci (healthy, torn and degenerated menisci) ranged from 68% to 80%. Diagnostic accuracy for healthy, torn and degenerated menisci was 87.50%, 86.96%, and 84.78%, respectively. Validation results from external dataset demonstrated that the accuracy of diagnosing torn and intact meniscus tear through 3.0T MRI images was higher than 80%, while the accuracy verified by arthroscopic surgery was 87.50%.ConclusionMask R–CNN effectively identified and diagnosed meniscal injuries, especially for tears that occurred in different parts of the meniscus. The recognition ability was admirable, and the diagnostic accuracy could be further improved with increased training sample size. Therefore, this deep learning model showed great potential in diagnosing meniscus injuries.Translational potential of this articleDeep learning model exerted unique effect in terms of reducing doctors’ workload and improving diagnostic accuracy. Injured and healthy menisci could be more accurately identified and classified based on training and learning datasets. This model could also distinguish torn from degenerated menisci, making it an effective tool for MRI-assisted diagnosis of meniscus injuries in clinical practice.

Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images.

Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively.

Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%.

Wall Cracks Detection in Aerial Images Using Improved Mask R-CNN

The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN methods were compared. Then, the Mask R-CNN method with the best performance and average precision of 0.34 was selected. Finally, based on the characteristics of cracks, the utilization ratio of Mask R-CNN to the underlying features was improved so that the average precision of 0.9 was achieved. It was found that the positioning accuracy and mask coverage rate of the proposed Mask R-CNN method are greatly improved. Also, it will be shown that using UAV is safer than manual detection because manual parameter setting is not required. In addition, the proposed detection method is expected to greatly reduce the cost and risk of manual detection of building exterior wall cracks and realize the efficient identification and accurate labeling of building exterior wall cracks.

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Recent technological advancements have been used to improve the quality of living in smart cities. At the same time, automated detection of vehicles can be utilized to reduce crime rate and improve public security. On the other hand, the automatic identification of vehicle license plate (LP) character becomes an essential process to recognize vehicles in real time scenarios, which can be achieved by the exploitation of optimal deep learning (DL) approaches. In this article, a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition (HMODL-ALPCR) technique has been presented for smart city environments. The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them. For effective LP detection process, mask regional convolutional neural network (Mask-RCNN) model is applied and the Inception with Residual Network (ResNet)-v2 as the baseline network. In addition, hybrid sunflower optimization with butterfly optimization algorithm (HSFO-BOA) is utilized for the hyperparameter tuning of the Inception-ResNetv2 model. Finally, Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs. The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

Deep Learning Based Hand Wrist Segmentation using Mask R-CNN

Deep learning is one of the trending technologies in computer vision to identify and classify objects. Deep learning is a subset of Machine Learning and Artificial Intelligence. Detecting and classifying the object was a challenging task in traditional computer vision techniques, and now there are numerous deep learning Techniques scaled up to achieve this. The primary purpose of the research is to detect and segment the human hand wrist region using deep learning methods. This research is widespread to deep learning enthusiasts who needs to segment custom objects using instance segmentation. We demonstrated a segmented hand wrist using the Mask Regional Convolutional Neural Network (R-CNN) technique with an average accuracy of 99.73%. This work also compares the performance evaluation of baseline and custom Hand Wrist Mask R-CNN. The achieved validation class loss is 0.00866 training and 0.02736 validation; both the values are comparatively deficient compared with baseline Mask R-CNN.