# COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images

arXiv: https://arxiv.org/abs/2302.02619 · HTML: https://mlsanity.com/paper/2302.02619/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 61 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation](https://mlsanity.com/paper/2004.12537/) (2020)
- [JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation](https://mlsanity.com/paper/2004.07054/) (2020)
- [Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis](https://mlsanity.com/paper/2003.05037/) (2020)
- [Lung Infection Quantification of COVID-19 in CT Images with Deep Learning](https://mlsanity.com/paper/2003.04655/) (2020)
- [Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images](https://mlsanity.com/paper/2004.06689/) (2020)
- [MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning](https://mlsanity.com/paper/1910.09308/) (2019)
- [COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN](https://mlsanity.com/paper/2012.05073/) (2020)
- [Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder](https://mlsanity.com/paper/2108.11720/) (2021)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool](https://mlsanity.com/paper/1806.04429/) (2018)
- [Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2009.08864/) (2020)
- [A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI](https://mlsanity.com/paper/2201.05373/) (2022)
- [IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN](https://mlsanity.com/paper/2202.04121/) (2022)
- [An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism](https://mlsanity.com/paper/2004.06673/) (2020)
- [Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN](https://mlsanity.com/paper/2211.16571/) (2022)
- [Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry](https://mlsanity.com/paper/1901.06091/) (2019)
- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images](https://mlsanity.com/paper/2004.03431/) (2020)

## Built on by (most cited first, 4 of 4)

- [RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis](https://mlsanity.com/paper/2410.01216/) (2024)
- [A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosis](https://mlsanity.com/paper/2405.12986/) (2024)
- [AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformers](https://mlsanity.com/paper/2305.00046/) (2023)
- [A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecasting](https://mlsanity.com/paper/2401.11621/) (2024)

## Abstract (arXiv)

COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient diagnostic systems may accurately identify infected patients and thus help control their spread. In this regard, a new two-stage analysis framework is developed to analyze minute irregularities of COVID-19 infection. A novel detection Convolutional Neural Network (CNN), STM-BRNet, is developed that incorporates the Split-Transform-Merge (STM) block and channel boosting (CB) to identify COVID-19 infected CT slices in the first stage. Each STM block extracts boundary and region-smoothing-specific features for COVID-19 infection detection. Moreover, the various boosted channels are obtained by introducing the new CB and Transfer Learning (TL) concept in STM blocks to capture small illumination and texture variations of COVID-19-specific images. The COVID-19 CTs are provided with new SA-CB-BRSeg segmentation CNN for delineating infection in images in the second stage. SA-CB-BRSeg methodically utilized smoothening and heterogeneous operations in the encoder and decoder to capture simultaneously COVID-19 specific patterns that are region homogeneity, texture variation, and boundaries. Additionally, the new CB concept is introduced in the decoder of SA-CB-BRSeg by combining additional channels using TL to learn the low contrast region. The proposed STM-BRNet and SA-CB-BRSeg yield considerable achievement in accuracy: 98.01 %, Recall: 98.12%, F-score: 98.11%, and Dice Similarity: 96.396%, IOU: 98.845 % for the COVID-19 infectious region, respectively. The proposed two-stage framework significantly increased performance compared to single-phase and other reported systems and reduced the burden on the radiologists.