Comparative Evaluation of Inception V3 and ResNet 50 for Pneumonia Prediction

Anuja  Bokhare; P.S.  Metkewar; Sonakshi  Ruhela; Madhav  Avasthi; Aafaq A.  Rather; Mohammad Shahnawaz  Shaikh; Raeesa  Bashir; Showkat A.  Dar

doi:10.6000/1929-6029.2026.15.06

Authors

Anuja Bokhare School of Data Science, Symbiosis Skills and Professional University, Pune-412101, Maharashtra, India
P.S. Metkewar School of Computer Science and Engineering, Department of Computer Science and Applications, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, India
Sonakshi Ruhela Department of Liberal Arts, Faculty of Psychological Science, Rochester Institute of Technology, Dubai, UAE
Madhav Avasthi MIQ Digital India pvt Ltd., Bengaluru, India
Aafaq A. Rather Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India
Mohammad Shahnawaz Shaikh Faculty of Engineering and Technology, Marwadi University, Rajkot, India
Raeesa Bashir Department of Mathematics and Statistics, Faculty of Science and Technology, Vishwakarma University, Pune, India
Showkat A. Dar Department of Computer Science and Engineering, GITAM University, Bangalore Campus-561203, India

DOI:

https://doi.org/10.6000/1929-6029.2026.15.06

Keywords:

Pneumonia Classification, Artificial Neural Network, Deep Learning Algorithms, Inception V3, Resnet50

Abstract

Pneumonia is a fatal respiratory infection that has become the leading cause of death among many people across the world. Its widespread has grabbed great attention making it a major topic for research under various domains. Its severity has led to the development of systems that can predict whether a patient has chances of being diagnosed with pneumonia or not, this is also called as computer aided diagnosis. However, current study intends to identify an Artificial Neural Network (ANN) model that has been able to provide the highest accuracy when it comes to predicting this life-threatening condition. The prediction was initially done with Machine learning techniques but with the introduction of ANN, it was observed that there are models that provided higher accuracy than the ML models. This study investigates how the concept of deep learning which is a vital part of ANN makes use of one of its most efficient models including Inception V3 and ResNet 50 for the prediction of pneumonia and compare their performance to suggest a better solution to the problem. Results indicate that ResNet50 offers clinically meaningful improvements in sensitivity and specificity, supporting its role as a decision-support tool for early pneumonia detection.

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