Prof. Vazralu Munnangi is an accomplished academician with over 20 years of rich experience in technical education, academic leadership, research mentorship, and student development. He completed his B. Tech in CSIT from Vignan’s Engineering College, Guntur (JNTU-H), M. Tech in Computer Science and Engineering from JNTU Kakinada, and is currently pursuing his Ph.D. at Vellore Institute of Technology (VIT), Vellore.
His academic and research specialization spans Image Processing, Wireless Sensor Networks, Data Science, Cyber Security, and IoT Systems. As a forward-thinking educator, he has significantly contributed to course design, laboratory development, introduction of new academic programs, and strengthening Outcome-Based Education (OBE) frameworks. He has also served on Board of Studies (BOS) and institutional curriculum design committees, supporting NAAC/NBA accreditation and academic quality systems.
Prof. Vazralu has guided numerous UG and PG projects, facilitated industry-dri
EDUCATION
Prof. Vazralu Munnangi is an accomplished academician with over 20 years of rich experience in technical education, academic leadership, research mentorship, and student development. He completed his B. Tech in CSIT from Vignan’s Engineering College, Guntur (JNTU-H), M. Tech in Computer Science and Engineering from JNTU Kakinada, and is currently pursuing his Ph.D. at Vellore Institute of Technology (VIT), Vellore
Multiclass Classification of Chest X-rays based Pulmonary Disorder Using a Specialized VGG-19 Deep Neural Network Vazralu M., Madiajagan M. Journal of Innovative Image Processing, 2025 Respiratory infections such as COVID-19, tuberculosis (TB) and pneumonia, remain important global health challenges, often requiring rapid and accurate diagnosis to prevent complications. Due to the visual similarities in chest X-ray (CXR) images, distinguishing between these diseases can be complex. In this study, we proposed, a deep learning (DL)-based model utilizing a customized VGG-19 architecture for multiclass classification of lung diseases, including COVID-19, pneumonia, TB, and healthy cases. A total of 5,928 CXR images were collected from open-access platforms, comprising COVID-19, pneumonia, TB, and normal cases. The dataset was pre-processed using bilateral filtering for noise suppression and Multiscale Retinex for image enhancement. FFurthermore, data augmentation and image resizing were also applied to increase robustness. When compared with the state-of-the-art techniques, the proposed method achieved a classification accuracy of 98.48% in identifying various lung disorders, with precision at 97% and an F1-score of 96%, indicating that it is an appropriate technique for computerized lung disease diagnosis in clinical environments.
