Open Access

ARTICLE

Modelling a Fused Deep Network Model for Pneumonia Prediction

M. A. Ramitha^*, N. Mohanasundaram, R. Santhosh

Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India

* Corresponding Author: M. A. Ramitha. Email:

Computer Systems Science and Engineering 2023, 46(3), 2725-2739. https://doi.org/10.32604/csse.2023.030504

Received 28 March 2022; Accepted 24 June 2022; Issue published 03 April 2023

Abstract

Deep Learning (DL) is known for its golden standard computing paradigm in the learning community. However, it turns out to be an extensively utilized computing approach in the ML field. Therefore, attaining superior outcomes over cognitive tasks based on human performance. The primary benefit of DL is its competency in learning massive data. The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully. Specifically, various DL approaches outperform the conventional ML approaches in real-time applications. Indeed, various research works are reviewed to understand the significance of the individual DL models and some computational complexity is observed. This may be due to the broader expertise and knowledge required for handling these models during the prediction process. This research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification purposes. This work incorporates a novel fused Squeeze and Excitation (SE) block with the ResNet model for pneumonia prediction and better accuracy. The expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive. The experimentation is carried out in Keras, and the model’s superiority is compared with various advanced approaches. The proposed model gives 90% prediction accuracy, 93% precision, 90% recall and 89% F1-measure. The proposed model shows a better trade-off compared to other approaches. The evaluation is done with the existing standard ResNet model, GoogleNet+ResNet+DenseNet, and different variants of ResNet models.

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