Computer Vision Research Articles

Understanding the Complexities of Tuberculosis: A Comprehensive Review of Drug-Resistant Strains in Pakistan

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a critical global health challenge, claiming over one million lives annually. Despite substantial progress in reducing TB incidence in developed countries post-World War II, it persists as a major cause of mortality, particularly in low-income populations. Socio-economic disparities significantly contribute to TB prevalence, exacerbating the uneven distribution of resources and healthcare access. Pakistan is among the nations severely impacted by TB, with eradication targets still unmet. All forms of TB are prevalent in the region, presenting symptoms such as fever, chronic cough, chills, and weight loss. This review assesses treatment outcomes and the prevalence of pulmonary TB in diagnosed patients in Pakistan. The airborne transmission of TB significantly drives its global burden. According to the World Health Organization (WHO), 2021 witnessed 1.2 million TB-related deaths and 9.9 million new cases. Diagnostic methods include immunological assays and molecular techniques, requiring sputum and blood sample analyses. Early diagnosis is crucial to prevent the emergence of drug-resistant strains. Pulmonary TB primarily spreads via respiratory pathways, with MTB infecting alveolar macrophages, leading to disease progression. Although studies on genetic susceptibility to TB have been conducted, conclusive evidence remains elusive. Computed Tomography (CT) provides detailed insights into TB manifestations but demands automation due to the high volume of data generated. The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB presents serious challenges to control and treatment. This review offers a comprehensive analysis of the epidemiology, pathogenesis, diagnosis, treatment, and prevention strategies for MDR and XDR TB. It highlights existing research gaps and proposes future directions to address these challenges. Advances in Artificial Intelligence (AI) and Computer Vision (CV) provide novel approaches for automating TB image analysis, facilitating scalable pre-clinical trials and improving disease management.

Pioneering automation in agricultural subsidy processing through deep learning for computer vision

ABSTRACT Validating farmers’ eligibility for subsidies under the European Union’s Common Agricultural Policy (CAP) requires substantial resources. An automated solution can minimize human errors and save time. This study leverages deep learning to automate the validation process using ground-level landscape images (GLLP), an approach not previously explored. We conducted two rigorous experiments to identify the optimal model architecture and assess the impact of data cleaning, accurate labelling, reduced feature ambiguity, and stricter photo collection protocols on performance. Our results reveal that while the ResNet-18 architecture achieved a recall of 0.61 on the original dataset, distinguishing between eligible and ineligible grasslands was challenging. By addressing image quality and enforcing standardized data collection, accuracy improved to 0.89 and recall to 0.94. Crucially, our research emphasizes the development and validation of effective data collection strategies, ensuring uniformity and high-quality data essential for accurate model performance. Our findings affirm the potential of deep learning to automate subsidy eligibility classification, contingent on resolving challenges related to the quality and standardization of GLLP data.

AI-Based Analysis of Archery Shooting Time from Anchoring to Release Using Pose Estimation and Computer Vision

This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase using pose estimation and accurately measures the shooting time by detecting the bowstring within the athlete’s facial bounding box, utilizing Canny edge detection and the probabilistic Hough transform. To ensure stability, low-pass filtering was applied to both the facial bounding box and pose estimation results, and an algorithm was implemented to handle intermittent bowstring detection due to various external factors. The proposed method was validated by comparing its results with expert manual measurements obtained using Dartfish software v2022 achieving a mean absolute error (MAE) of 0.34 s and an R2 score of 0.95. This demonstrates a significant improvement compared to the bowstring-only method, which resulted in an MAE of 1.4 s and an R2 score of 0.89. Previous research has demonstrated a correlation between shooting time and arrow accuracy. Therefore, this method can provide real-time feedback to athletes, overcoming the limitations of traditional manual measurement techniques. It enables immediate technical adjustments during training, which can contribute to overall performance improvement.

Advances in LUCAS Copernicus 2022: enhancing Earth observations with comprehensive in situ data on EU land cover and use

Abstract. The Land Use/Cover Area frame Survey (LUCAS) of the European Union (EU) presents a rich resource for detailed understanding of land cover and use, making it invaluable for Earth observation (EO) applications. This paper discusses the recent enhancements and improvements in the LUCAS Copernicus module, particularly the data collection process of 2022, its protocol simplifications, and geometry definitions compared to the 2018 survey and data. With approximately 150 000 polygons collected in 2022, an increase from 60 000 in 2018, the LUCAS Copernicus 2022 data provide a unique and comprehensive in situ dataset for EO applications. The protocol simplification also facilitates a faster and more efficient data collection process. In 2022, there were 137 966 polygons generated out of the original 149 408 LUCAS Copernicus points, which means that 92.3 % of the points were actually surveyed. The data have 82 land cover classes for the Copernicus module that map to 88 classes up to the LUCAS level-3 legend. For land use the data have 40 classes, along with 18 classes of land use types. The dataset is available for download (product IDentification – PID: http://data.europa.eu/89h/e3fe3cd0-44db-470e-8769-172a8b9e8874; European Commission, 2023). The paper elaborates further on the implications of these enhancements and the need for continuous harmonization to ensure semantic consistency and temporal usability of data across different periods. Moreover, it calls for additional studies exploring the potential of the collected data, especially in the context of remote sensing and computer vision. It ends with a discussion of future data usage and dissemination strategies.

Novel variant transformer-based method for aluminum profile surface defect detection

Abstract The detection of surface defects on metal plays a pivotal role in the evaluation of product quality for aluminum profiles. So far, the most prevalent approach for detecting metal surface defects is still through the utilization of traditional conventional neural networks (CNNs). However, transformer-based methods have made notable improvements in computer vision in recent years, exhibiting superiority over traditional CNNs in the majority of tasks. In regard to aluminum surface defect image characteristics, such as intricate textures, few defect samples, and large-scale differences in various defects, traditional CNNs have difficulty further improving performance. This paper proposes a novel method based on a variant transformer for surface defect detection of aluminum profiles, which combines a traditional CNN and a transformer architecture with a deformable attention module (DAM). The DAM better focuses on part of critical sampling points around the reference point, which alleviates the unacceptable complexity in high-resolution feature maps and enhances the recognition effect of small target defects. The image stitching method proposed uses defect-free samples to generate new samples, which partially mitigates the issue of class imbalance. Moreover, we carried out supplementary experiments to confirm the effectiveness of transfer learning in training small-scale datasets. Compared with the baseline, the mean average precision (mAP) was improved by 3.32%. Extensive experimental results demonstrate the efficacy of our method in accurately detecting surface defects of aluminum profiles and significantly improving the detection of small target defects.

Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy

Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed framework utilizes the EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images. By harnessing advanced techniques in computer vision and machine learning, the proposed model aims to deliver precise and dependable DR diagnoses. Continuous testing and experimentation shows to the efficiency of the architecture, showcasing promising outcomes that could help in the transformation of both diagnosing and treatment of DR. This framework takes help from the EfficientNet Machine Learning algorithms and employing advanced CNN layering techniques. The dataset utilized in this study is titled ’Diagnosis of Diabetic Retinopathy’ and is sourced from Kaggle. It consists of 35,108 retinal images, classified into five categories: No Diabetic Retinopathy (DR), Mild DR, Moderate DR, Severe DR, and Proliferative DR. Through rigorous testing, the framework yields impressive results, boasting an average accuracy of 86.53% and a loss rate of 0.5663. A comparison with alternative approaches underscores the effectiveness of EfficientNet in handling classification tasks for diabetic retinopathy, particularly highlighting its high accuracy and generalizability across DR severity levels. These findings highlight the framework’s potential to significantly advance the field of DR diagnosis, given more advanced datasets and more training resources which leads it to be offering clinicians a powerful tool for early intervention and improved patient outcomes.

Advancements in nutty quality: Segmentation for enhanced monitoring and determination

Segmentation of nut images plays a vital role in computer vision and agricultural applications. Precise segmentation enables the extraction and analysis of essential information about the nuts, supporting quality evaluation, yield estimation, and automated sorting processes. This study explores nuts image segmentation utilizing the cuckoo search algorithm. The cuckoo search algorithm, a nature-inspired optimization technique, is introduced to enhance the segmentation process, potentially optimizing parameters or guiding the segmentation algorithms. Performance evaluation emphasizes metrics such as MSE, IoU, and dice coefficient. CSA (cuckoo search algorithm) demonstrates superior results, showcasing its effectiveness in automated nuts segmentation. This research contributes to the advancement of nut image analysis, providing insights into segmentation methodologies that can enhance automated processes in agriculture and food industry applications. The findings underscore the significance of employing advanced algorithms like CSA for accurate and efficient segmentation of nuts in images.

Unlocking Organizational Success: A Systematic Literature Review of Superintendent Selection Strategies, Core Competencies, and Emerging Technologies in the Construction Industry

An organization’s success depends on its ability to attract and retain skilled personnel. Superintendents play a critical role in overseeing project sites in the construction industry and can adapt to the increasingly complicated requirements of modern construction projects. This study examines traditional and modern personnel selection methods to determine effective tactics, essential competencies, and emerging trends regarding supervisory personnel. The research methodology follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. First, this study examines traditional and modern selection methods used by organizations and engineering firms to provide a comprehensive overview of the topic and assist in selecting appropriate staff recruitment procedures. Second, the Web of Science, Scopus, and Google Scholar databases were reviewed to identify superintendent selection approaches and competencies, over the period January 2000 to September 2024. A total of 22 relevant papers were analyzed. Superintendent selection processes included questionnaires (57%), interviews (26%), literature reviews (14%), and data-driven AI tools (3%). Forty competency criteria were identified, with the top five being knowledge, communication skills, leadership, health and safety expertise, and commitment. As a result, novel approaches employing Industry 4.0 technologies, including virtual reality (VR), wearable sensing devices (WSDs), natural language processing (NLP), blockchain, and computer vision, are recommended. These findings support a better understanding of how best to identify the most qualified supervisory personnel and provides enhanced methods for evaluating job applicants.

Pattern recognition and image segmentation based on some novel fuzzy similarity measures

ABSTRACT Pattern recognition and image segmentation are challenging tasks in computer vision, with applications ranging from object detection to medical imaging. Fuzzy comparison measures emerge as an effective tool in handling these tasks, especially when two feature spaces are associated with uncertainty. Recently, many fuzzy comparison measures have been proposed and applied to different kinds of real-life problems. However, these measures have certain drawbacks because of their incapability to classify very similar pairs of objects, which may not produce effective results in the problems related to pattern recognition and image segmentation. This paper investigates the derivation of new fuzzy similarity measures. The proposed measures bear a continuous nature, allowing for subtle differences and smooth transitions between levels of similarity. This characteristic enhances the precise interpretation of linguistic variables. Moreover, pattern recognition and image segmentation techniques that primarily utilised proposed fuzzy similarity measures have been developed. Certain patterns and images are considered for the implementation of the proposed techniques. The results revealed that the proposed measures are advantageous over existing similarity measures, highlighting their potential for improving performance in pattern recognition and image segmentation.

Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces

Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification.

Improving the Efficiency of Defect Image Identification During Computer Decoding of Digital Radiographic Images of Welded Joints of Hazardous Production Facilities

This article is devoted to improving the efficiency of flaw image identification during computer decoding of digital radiographic images. The paper studies the problem of segmentation of flaw images. Models of segmentation of flaw images on a radiographic image are studied for both manual and computer decoding. The difference between algorithms for searching and identifying groups, clusters, chains of pores, slag and metal inclusions from manual decoding of images is shown. Algorithms for the search and identification of flaws for use in digital radiography complexes have been developed and experimentally tested at HSC KARS. The convergence of the results of computer and manual decryption was 0,85.

The use of ChatGPT in the dermatological field: a narrative review.

Artificial Intelligence (AI) encompasses the development of computer systems capable of tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Over time, numerous applications have emerged, with AI's integration into medicine marking a significant leap forward in healthcare delivery, diagnosis, and treatment. Among medical specialties, dermatology stands at the forefront of AI advancements, leveraging machine learning and deep learning to enhance dermatologists' abilities and improve patient care. ChatGPT is an advanced language model by OpenAI, originally designed for conversations, which has expanded its utility into diverse fields, including healthcare and dermatology. In this context, the aim of this review article was exploring the synergistic relationship between ChatGPT and dermatology, examining how this innovative AI model is reshaping skin health management, its potential applications, preliminary data on its efficiency and accuracy, as well as ethical and legal concerns related to the use of its tool.

Melanoma toolkit for early detection for primary care clinicians: a 1-year follow-up on outcomes

IntroductionPrimary care providers or clinicians (PCPs) have the potential to assist dermatologists in screening patients at risk for skin cancer, but require training to appropriately identify higher-risk patients, perform skin checks, recognize and biopsy concerning lesions, interpret pathology results, document the exam, and bill for the service. Very few validated dermatology training programs exist for PCPs and those that are available focus primarily on one emphasis area, which results in variable efficacy and single-topic limited scope.MethodsWe have created a free, online, continuing education program (Melanoma Toolkit for Early Detection, MTED) that allows learners to choose from a variety of multimedia tools (image recognition, videos, written material, in-person seminars, self-tests, etc.) that suits their learning style and time availability. Here we present the toolkit, the development and validation of the curriculum, and report on 1-year outcomes of a nested survey study. Because the goal of the program is to maximize participation by allowing PCPs to tailor their experience to their own needs and interests, the majority of participants did not complete every element of the program.ResultsA total of 8,683 PCPs have accessed at least one element of the toolkit from 2019–2024. Participants completed a pre-survey, online training module, and post-survey that included self-reported screening behaviors, changes in confidence, and malignant and benign lesion categorization based on clinical images. A total of 139 pre-surveys and 92 post-surveys were completed, including 55 matched participants that completed both the pre- and post-training surveys. There were significant improvements in PCP confidence (P < 0.001) and malignant (P < 0.001) and benign image (P = 0.029) identification respectively.DiscussionPCPs may serve as a valuable aid in skin cancer screening efforts, but additional studies are needed to evaluate the impact of these curricula in clinical practice.

Reviving Brasília Through Analyzing Historical Aerial Photographs

This study presents an optimized method for processing and retrieving digital data from 1950s aerial photographs of the Plano Piloto area in Brasília. In analogous contexts, internal orientation parameters are often unavailable, and external orientation parameters must be obtained through non-standard approaches, as the identification and geolocation of control points in the field becomes impractical in substantially altered landscapes. Nevertheless, advancements in computer vision techniques, integrated into modern photogrammetry software such as Agisoft Metashape®, provide a viable solution for mapping historical analog aerial photographs. The software successfully processed 156 aerial photographs, using 17 control points, yielding a historical orthoimage and a digital surface model with medium-scale accuracy. The generated model enabled precise identification of key features, including vegetation cover, land use, and terrain characteristics, providing valuable insights for the analysis of land occupation dynamics in the study area. The proposed methodology exhibits substantial potential for replication in similar cases, establishing a robust framework for the processing of historical aerial imagery.

Decoding skin cancer classification: perspectives, insights, and advances through researchers’ lens

Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes. In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. This survey analyzes 107 research papers published over the last 18 years, providing a thorough evaluation of advancements in classification techniques, with a focus on the growing integration of computer vision and artificial intelligence (AI) in enhancing diagnostic accuracy and reliability. The paper begins by presenting an overview of the fundamental concepts of skin cancer, addressing underlying challenges in accurate classification, and highlighting the limitations of traditional diagnostic methods. Extensive examination is devoted to a range of datasets, including the HAM10000 and the ISIC archive, among others, commonly employed by researchers. The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. The survey also sheds light on the various hybrid and multimodal techniques employed for classification. By critically analyzing each approach and highlighting its limitations, this survey provides researchers with valuable insights into the latest advancements, trends, and gaps in skin cancer classification. Moreover, it offers clinicians practical knowledge on the integration of AI tools to enhance diagnostic decision-making processes. This comprehensive analysis aims to bridge the gap between research and clinical practice, serving as a guide for the AI community to further advance the state-of-the-art in skin cancer classification systems.

Visual Story Generation Model Guided by Multi Granularity Image Information

Visual story generation, which involves generating short stories from sequential images, has become a core task at the intersection of computer vision and natural language processing. However, existing methods suffer from a bias in the concept predicates predicted, leading to a semantic gap between the generated stories and the images. This paper proposes a novel visual story generation model that utilizes muliti granularity image information to guide the generation process and correct the bias in concept predicates, resulting in more image-consistent stories. The proposed model consists of two stages: In the first stage, a set of concepts predicates is predicted from the image and enriched with external knowledge, and the most suitable concepts for story generation are selected. In the second stage, fine-grained image information are utilized to integrate image information into the story generation module, improving the bias in concept predicates. The image theme information and the generated results of previous moments are used as prompts to guide the story generation module. Experimental results show that the proposed model outperforms baseline models in all evaluation metrics. Specifically, the BLEU-1, BLEU-2, BLEU-3, and BLEU-4 metrics are improved by 4.0, 3.8, 3.02, and 1.98 percentage points, respectively, and the METEOR metric is improved by 1.4 percentage points. The generated stories are more consistent with the image content, maintain a consistent theme, and enhance coherence between contexts.

Loop-mediated isothermal amplification assays for the detection of antimicrobial resistance elements in Vibrio cholera

BackgroundThe bacterium Vibrio cholerae causes diarrheal illness and can acquire genetic material leading to multiple drug resistance (MDR). Rapid detection of resistance-conferring mobile genetic elements helps avoid the prescription of ineffective antibiotics for specific strains. Colorimetric loop-mediated isothermal amplification (LAMP) assays provide a rapid and cost-effective means for detection at point-of-care since they do not require specialized equipment, require limited expertise to perform, and can take less than 30 min to perform in resource limited regions. LAMP output is a color change that can be viewed by eye, but it can be difficult to design primer sets, determine target specificity, and interpret subjective color changes.MethodsWe developed an algorithm for the in silico design and evaluation of LAMP assays within the open-source PCR Signature Erosion Tool (PSET) and a computer vision application for the quantitative analysis of colorimetric outputs. First, Primer3 calculates LAMP primer sequence candidates with settings based on GC-content optimization. Next, PSET aligns the primer sequences of each assay against large sequence databases to calculate sufficient sequence similarity, coverage, and primer arrangement to the intended taxa, ultimately generating a confusion matrix. Finally, we tested assay candidates in the laboratory against synthetic constructs.ResultsAs an example, we generated new LAMP assays targeting drug resistance in V. cholerae and evaluated existing ones from the literature based on in silico target specificity and in vitro testing. Improvements in the design and testing of LAMP assays, with heightened target specificity and a simple analysis platform, increase utility for in-field applications. Overall, 9 of the 16 tested LAMP assays had positive signal through visual and computer vision-based detection methods developed here. Here we show LAMP assays tested on synthetic AMR gene targets for aph(6), varG, floR, qnrVC5, and almG, which allow for resistance to aminoglycosides, penicillins, carbapenems, phenicols, fluoroquinolones, and polymyxins respectively.

ElementaryCQT: A New Dataset and its Deep Learning Analysis for 2D Geometric Shape Recognition

ElementaryCQT: A New Dataset and its Deep Learning Analysis for 2D Geometric Shape Recognition

Contributions of hemispheric dynamics in visual word recognition: uncovering familiarity effects through lateralized priming

IntroductionThis investigation aimed to explore interhemispheric interactions in visual word processing with a focus on proficiency development. Given the asymmetrical specialization in visual word processing across hemispheres, the study hypothesized that the primary hemisphere predominantly regulates interhemispheric interactions. The familiarity effect, serving as a measure of visual word processing proficiency, was examined to determine how proficiency influences these interactions.MethodsA primed-lateralized lexical decision task with a stimulus onset asynchrony (SOA) of 100 ms was employed. The task involved presenting primes and targets in parafoveal visual fields (left visual field/right visual field) to assess behavioral responses. By manipulating prime and target visual field locations, the study aimed to evaluate both inter- and intrahemispheric interactions during visual word processing.ResultsThe findings revealed a significant interhemispheric familiarity effect in response times when the left visual field (LVF)/right hemisphere (RH) served as the prime and the right visual field (RVF)/left hemisphere (LH) as the target. Additionally, a significant intrahemispheric familiarity effect was observed within the LVF/RH condition, suggesting a prominent role of the RH in visual-perceptual processing during the development of visual word recognition proficiency.DiscussionThese results provide compelling evidence for asymmetric specialization between the hemispheres in visual word processing. The significant inter- and intrahemispheric familiarity effects underscore the importance of RH visual-perceptual processing in proficiency development. These insights enhance our understanding of interhemispheric dynamics in the evolution of visual word recognition proficiency, highlighting the complex coordination between hemispheres in facilitating fluent visual word processing.

Evaluation of artificial intelligence and optical image recognition techniques used in OneIso, an off-axis Winston-Lutz quality assurance phantom.

Single-isocenter multitarget (SIMT) stereotactic-radiosurgery (SRS) has recently emerged as a powerful treatment regimen for intracranial tumors. With high specificity, SIMT SRS allows for rapid, high-dose delivery while maintaining integrity of adjacent healthy tissues and minimizing neurocognitive damage to patients. Highly robust and accurate quality assurance (QA) tests are critical to minimize off-targets and damage to surrounding healthy tissues. We have developed a novel QA phantom, named OneIso, to accurately and precisely measure off-axis accuracy, via off-axis Winston-Lutz (OAWL), to assist SIMT SRS programs. In this study, a comparison of three different quantitative numerical methods were performed for isolating and measuring the position of ball-bearings (BBs) used in the OAWL measurement. The three methods evaluated were: 1) feature extraction technique combined with manual intervention 2) a proprietary software utilizing optical image recognition (OIR) techniques, and 3) a machine learning (ML) model employing convolutional neural networks (CNNs). These methods were used to analyze OAWL datasets gathered from a OneIso phantom deployed on a Varian TrueBeam. The precision of localizing positional BBs within the OneIso QA phantom, analysis speed, and robustness were compared across the methods. Significantly, the trained ML model utilizing CNNs was found to exhibit superior precision, analysis speed, and efficiency compared to the other two methods. These results highlight the benefit in shifting from manual and OIR methods to that of ML techniques. The incorporation of CNNs in automated QA analysis can achieve improved precision, allowing for more rapid and wider adoption of SIMT SRS for treating intracranial metastases while preserving integrity of surrounding healthy tissues.