The patent titled, System and Method for Detecting a Face Mask on the Face of a Person with application number: 562697 by Assistant Professor, Dr Anirban Ghosh and his students from the department Ms J Keerthi Tanvitha, Ms K Poojitha, Ms T Pallavi Sri, has come up with a novel idea that focuses on developing a system and method for detecting face masks on individuals before they enter a gated space. Their research uses image capture, face detection, mask-wearing verification, and temperature sensing to ensure compliance with health and safety protocols.

Brief Abstract

The research focuses on developing a system and method for detecting face masks on individuals before they enter a gated space. The system integrates image capture, face detection, mask-wearing verification, and temperature sensing to ensure compliance with health and safety protocols. It utilizes artificial intelligence and sensor technology to determine whether a person is wearing a mask correctly and allows or denies entry based on preset criteria. The system also includes an aliveness detection module to ensure that a real person is being scanned and prevents tampering or spoofing.

Explanation in Layperson’s Terms

Imagine you are entering a building where you must wear a mask and have your temperature checked. Instead of a security guard manually checking each person, this system does it automatically and without contact.

Here’s how it works:

• A camera captures your face as you approach the entrance.
• A software program checks if you are wearing a mask correctly (covering your nose and mouth).
• A temperature sensor scans your body temperature.
• If your mask is worn properly and your temperature is normal, the door opens and you can enter.
• If you are not wearing a mask correctly or have a high temperature, an alert is triggered, and entry is denied.

This system is especially useful in hospitals, offices, schools, airports, and malls to ensure safety without requiring human intervention.

Practical and Social Implications

This system plays a crucial role in public health by ensuring compliance with mask-wearing protocols, especially in high-traffic areas like hospitals, offices, and airports. By automating the detection process, it reduces reliance on manual checks, minimizing human error and lowering exposure risks for security personnel. Additionally, it streamlines entry procedures, preventing congestion at access points. While the technology enhances safety, it also raises concerns about privacy and data security, making it essential to implement ethical safeguards. Beyond COVID-19, this system can be adapted for future health crises requiring similar preventive measures.

Future Research Plans

Future improvements will focus on enhancing accuracy using AI to recognize different mask types and improve real-time detection. Expanding the system to include biometric health screening, such as cough detection and respiratory analysis, could make it a broader health monitoring tool. Integration with facial recognition and automated access systems will further strengthen security applications. Additionally, optimizing the system with edge computing will enable faster, more efficient processing. As the need for contactless health monitoring grows, refining this technology will ensure its continued relevance beyond COVID-19.

Dr Manjula R, Assistant Professor, Department of Computer Science and Engineering, and Dr Anirbhan Ghosh, Assistant Professor, Department of Electronics and Communication Engineering, has recently had their patent published titled “A System for Analyzing Electromagnetic Wave Scattering Path Loss in a Tissue and a Method Thereof” with Application no: 202541001730.

The faculty duo has revolutionised cardiac diagnostics and real-time health monitoring through their invention. This innovative system analyses electromagnetic wave scattering in biological tissues, using terahertz (THz) frequencies to optimise nanosensor communication and path loss analysis. By leveraging cutting-edge technology, it enables advanced biomedical devices for precise physiological monitoring and safer, more reliable in-vivo communication systems. A step forward for heart health and medical breakthroughs, this invention bridges the gap between technology and life-saving healthcare solutions.

The research team from the Department of Electronics and Communication Engineering has published a paper titled “RadiomixNet: Integrating Radiomics and Feature Extraction for Advanced Pneumonia Diagnosis” in the journal IEEE Access with an impact factor of 3.4. Prof. Siva Sankar Yellampalli, Professor of Practice, and Mr Rahul Gowtham, PhD Scholar, have worked on RadiomixNet, a smart computer-assisted system designed to help doctors diagnose pneumonia more accurately using chest X-ray images.

Abstract

The research presents RadiomixNet, a pneumonia diagnosis framework integrating radiomics-based feature extraction with advanced classification techniques. Chest X-ray images are pre-processed using denoising, resizing, and enhancement methods to ensure uniformity and high image quality. Radiomics features are extracted using Gray Level Co-Occurrence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), and Gray Level Dependence Matrix (GLDM). Power Spectral Density (PSD) analysis using Burg, Yule Walker, and Welch techniques enhances the understanding of frequency characteristics within the radiomics feature matrices. To classify pneumonia cases, machine learning classifiers such as Bernoulli Naïve Bayes, Random Subspace Boost, Quadratic Discriminant, and Gradient Boosting are employed. Among these, Gradient Boosting demonstrated superior performance, achieving a Cohen’s Kappa of 0.93, MCC of 0.88, Youden’s Index of 0.82, and a Log Loss of 0.27. The proposed methodology enhances diagnostic accuracy, reduces variability in pneumonia detection, and provides a structured approach to feature-based pneumonia classification.

Explanation of the Research in Layperson’s Terms

Traditional diagnosis relies on a doctor visually examining the X-ray, which can sometimes lead to misinterpretations. RadiomixNet improves this process by using advanced image processing and artificial intelligence (AI) techniques.

1. Preprocessing the X-rays – Before analysis, we clean the images by removing noise (unwanted distortions), adjusting brightness, and resizing them to a standard format. This ensures all images are high quality and uniform.
2. Generating More Training Data – Since AI models need a large amount of data to learn effectively, we use Generative Adversarial Networks (GANs) to create additional synthetic X-ray images. This helps balance the dataset and improve the model’s ability to detect pneumonia accurately.
3. Extracting Hidden Patterns – The system breaks down X-ray images into tiny texture and shape details using advanced techniques like GLCM, GLSZM, GLRLM, and GLDM. These methods capture the structure of the lungs and highlight patterns that indicate pneumonia.
4. Analysing Frequency Components – Similar to how an audio equalizer separates different sound frequencies, we analyze the X-ray’s frequency components using techniques like Burg PSD, Yule Walker PSD, and Welch PSD. This helps uncover hidden details in the images that may not be visible to the human eye.
5. Making the Final Diagnosis – After extracting these detailed features, we use AI models to classify the images as “pneumonia” or “healthy.” We tested different models, including Naïve Bayes, Random Subspace Boost, Quadratic Discriminant, and Gradient Boosting. Among them, Gradient Boosting performed the best, making the most accurate predictions.
6. Evaluating Accuracy – To ensure the system is reliable, we used various accuracy-checking methods such as Cohen’s Kappa, Matthews Correlation Coefficient (MCC), Sensitivity, Specificity, Log Loss, and Brier Score.

Practical Implementation/Social Implications of the Research

Practical Implementation:

RadiomixNet has the potential to be integrated into real-world healthcare systems to assist in pneumonia diagnosis. Its implementation can take place in various ways:

1. Hospital Integration – RadiomixNet can be deployed in hospitals as a decision-support tool for radiologists. By analysing chest X-rays in real time, it can provide secondary validation, reducing diagnostic errors and improving accuracy in pneumonia detection.
2. Telemedicine and Remote Diagnosis – The system can be integrated into telemedicine platforms, allowing doctors in rural or under-resourced areas to diagnose pneumonia remotely. Patients can upload their X-ray images, and RadiomixNet can assist in providing a preliminary diagnosis.
3. Medical Imaging Centers – Radiology centers can incorporate RadiomixNet into their existing Picture Archiving and Communication Systems (PACS) to enhance diagnostic efficiency, reduce the workload of radiologists, and provide automated analysis.
4. Edge Computing in Low-Resource Settings – Unlike deep learning models that require expensive GPUs, RadiomixNet is optimized for standard computing hardware. This makes it feasible for implementation in clinics and hospitals that lack high-end computational resources.
5. Clinical Trials and Further Validation – Pilot studies in hospitals can validate RadiomixNet’s accuracy and reliability before widespread deployment. The system can be fine-tuned based on real-world patient data to improve its performance across diverse populations.

Social Implications:

1. Early and Accurate Diagnosis – By improving pneumonia detection, RadiomixNet can enable earlier treatment, reducing complications and mortality rates, especially in high-risk populations such as children, the elderly, and immunocompromised individuals.
2. Reducing Radiologist Workload – With increasing patient loads, radiologists often face diagnostic fatigue. RadiomixNet can act as an assistant, helping them focus on complex cases while automating routine pneumonia detection.
3. Bridging the Healthcare Gap – In developing countries where expert radiologists are scarce, RadiomixNet can assist general practitioners and healthcare workers in diagnosing pneumonia without requiring extensive radiology expertise.
4. Affordable and Scalable Solution – Since the system does not require expensive hardware, it can be implemented in low-resource settings, making advanced pneumonia detection accessible to a broader population.
5. Pandemic Preparedness – Pneumonia is a major complication of respiratory infections like COVID-19. RadiomixNet can be adapted to detect pneumonia-related lung infections, aiding in large-scale screening during outbreaks.

By integrating RadiomixNet into healthcare systems, we can enhance diagnostic accuracy, improve patient outcomes, and make pneumonia diagnosis more accessible and efficient globally.

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RadiomixNet Implementation Framework

In the patent titled “A System and a Method For Real-Time Pneumonia Diagnosis On a Resource- Constrained Hardware Platform,” authored by Prof. Siva Sankar Yellampalli from the Department of ECE and his research scholars – Mr Rahul Gowtham Poola and P L Lahari, a novel diagnostic solution is presented to enhance pneumonia detection in low-resource settings. With Application No: 202441084727, this research explores the integration of advanced deep learning techniques with a compact microcontroller-based system, providing an innovative approach to improve healthcare accessibility and prompt medical intervention.

Abstract:

The research focuses on the development of an innovative system for real-time pneumonia diagnosis leveraging advanced deep learning techniques integrated with edge computing technology. The proposed solution employs the MAX78000 microcontroller, a resource-constrained hardware platform, to deploy a sophisticated neural network model capable of analyzing chest X-ray images. The invention addresses the pressing need for accessible, cost-effective, and efficient diagnostic tools in under-resourced and remote environments. The system encompasses a complete diagnostic pipeline, including image acquisition via an onboard parallel camera module, real-time image processing, and display of results on a 3.5″ touch-enabled TFT screen. The deep learning model, optimized for the constraints of the MAX78000, performs real-time classification of chest X-ray images into either normal or pneumonia-affected categories. By operating entirely on-device, the system eliminates the need for high-power servers or internet connectivity, thereby reducing latency and dependency on external infrastructure. This research emphasizes portability, energy efficiency, and low-cost deployment, making the solution highly suitable for primary healthcare facilities, rural clinics, mobile health units, and disaster-response scenarios. With the ability to deliver immediate, accurate diagnoses, the device significantly enhances clinical decision-making and enables timely medical intervention. Additionally, the scalable and adaptable design of the system opens possibilities for broader medical imaging applications, extending its utility beyond pneumonia diagnostics. Experimental results showcase the performance of the neural network model, demonstrating prediction accuracies ranging between 66% and 97% for different test cases on the MAX78000 microcontroller. These findings underline the potential of the proposed system as a transformative tool for advancing point-of-care diagnostics in low-resource settings.

Explanation in Layperson’s terms.

The research presents a compact, affordable device that helps doctors quickly detect pneumonia by analyzing chest X-ray images in real-time. It uses advanced artificial intelligence (AI) technology, called deep learning, to examine the X-rays and determine whether a patient has pneumonia or not. What makes this device special is that it works entirely on a small, low-power microcontroller called the MAX78000, instead of needing powerful computers or internet access. The process begins when the device captures a chest X-ray image using its built-in camera. Then, the AI model, which has been trained to recognize patterns associated with pneumonia, analyzes the image. The results are displayed instantly on a small screen, allowing healthcare providers to make quick decisions. This real-time diagnosis can be life-saving, especially in emergency or rural settings where access to advanced medical equipment or high-speed internet is limited. Technically, this system combines AI and edge computing, meaning all the heavy processing happens directly on the device rather than in remote servers. This design keeps costs low, ensures patient data privacy, and makes the device highly portable and energy-efficient. The technology can work even in places with unreliable electricity, making it ideal for use in mobile health units, rural clinics, or disaster zones. Additionally, the invention can be adapted for diagnosing other diseases, showcasing its versatility in improving healthcare globally.

Practical Implementation

This research can be practically implemented as a compact, standalone device for diagnosing pneumonia in healthcare settings where access to advanced medical equipment is limited. It works as follows:

• Deployment in Rural Clinics and Mobile Health Units: The device can be used in clinics in remote or underserved areas where large X-ray machines and advanced computing resources are unavailable. It provides on-the-spot diagnosis.
• Point-of-Care Diagnostics: The portability and integration of image acquisition, AI-based processing, and display into a single unit make it ideal for bedside use in hospitals or during emergency care.
• Disaster Response: Its low-power and internet-free design make it a critical tool in disaster zones, refugee camps, or any setting where power and connectivity are unreliable.
• Telemedicine Integration: The device can complement telemedicine by providing accurate diagnostic results to remote doctors, helping bridge the gap between frontline healthcare workers and specialists.

Social Implications

Improved Access to Healthcare: By making pneumonia diagnosis accessible in rural and underserved regions, this device can drastically reduce the gap in healthcare services between urban and remote areas. It empowers healthcare providers in low-resource settings to deliver timely diagnoses.

• Affordability: The use of a low-cost microcontroller ensures that the device is affordable for governments and healthcare organizations, particularly in developing countries. This can enhance healthcare access for low-income populations.
• Reduced Mortality Rates: Pneumonia is a leading cause of death in children under five and elderly individuals, especially in low-income countries. This device’s ability to provide real-time, accurate diagnosis allows for earlier intervention and treatment, potentially saving countless lives.
• Privacy and Security: Since all data is processed locally on the device, it ensures patient privacy by eliminating the need to transfer sensitive medical data to cloud servers, addressing concerns about data security.
• Scalability: The underlying technology can be adapted to diagnose other diseases, creating a broader impact on global health. For example, similar systems could be used for tuberculosis, COVID-19, or other respiratory conditions, further enhancing healthcare infrastructure.

By addressing critical gaps in diagnostic capabilities and ensuring accessibility and affordability, this research has the potential to transform healthcare delivery and improve quality of life, especially in marginalized communities.

Collaborations:

Rahul Gowtham Poola, Ph.D Scholar, Dept of ECE, SRM University-AP

P.L. Lahari, Ph.D Scholar, Dept of ECE, SRM University-AP

Prof. Siva Sankar Yellampalli, Professor of Practice, Dept of ECE, SRM University-AP

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SRM University-AP formalised a significant Memorandum of Understanding (MoU) with the Sibar Institute of Dental Sciences, Guntur. The MoU is aimed at advancing the field of Biomedical Engineering & Biomedical Sciences and leverage SRM University-AP’s technical expertise alongside the medical proficiency of Sibar Institute, to foster groundbreaking innovations.

The MoU was signed in a ceremony by Principal, Dr B Venkat Ramana Reddy from Sibar Institute of Dental Sciences and Registrar, Dr R Premkumar from SRM University-AP. Notable attendees included Vice Chancellor, Prof. Manoj K Arora, Dean-SEAS Prof. C V Tomy; Dean-Research Prof. Ranjit Thapa; Head of the Department of ECE, Dr K A Sunitha; Chief Medical Officer, Dr Raju Dudam from the SRM AP Medical Centre, alongside faculty from the Departments of Computer Science and Engineering, Electronics and Communication Engineering, Biological Sciences and Chemistry and medical practitioners from Sibar Institute of Dental Sciences including Dean, Dr L Krishna Prasad; Dr K Kiran Kumar, Prof and Head, Department of Oral Pathology; Dr P Chandrashekhar, Professor- Department of Pathology and Dr D Ravinath Professor & Head, Department of Peridontics.

During the event, Vice Chancellor Prof. Manoj K. Arora pointed out the university’s rapid growth in both research and academic realms. He highlighted SRM AP‘s collaborations with esteemed institutions abroad stressing on the varsity’s commitment to interdisciplinary learning and research. Prof. Arora stated, “By merging engineering brilliance with medical sciences, we aim to achieve breakthroughs that will significantly benefit the society.”

Registrar Dr R Premkumar articulated the university’s dedication to research and its mission to bring transformational change. He remarked, “Our Faculty is our greatest asset, and we are prepared to extend all support to foster a transformative change in the society.”

Discussions from Dean-Research Prof. Ranjit Thapa, Dean-SEAS Prof. C V Tomy, and Associate Dean ( Admission Outreach and Research Collaborations), Prof. Jayaseelan Murugaiyan, on the collaborative research opportunities and clinical studies between the two institutions added significant value to the partnership.

Dean, Dr L Krishna Prasad from Sibar Institute of Dental Sciences expressed his enthusiasm in partnering with SRM AP and looked forward to this collaborative engagement to make significant breakthrough in the field of medical sciences and research.

Dr K A Sunitha highlighted the potential for knowledge exchange through this partnership, noting plans for joint data collection and analysis. The collaboration will extend to community welfare too, wherein dental health camps in the six villages adopted by SRM AP will be offered.

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