|
Sejuti & Islam (2023)
|
CTs image |
CNN–KNN |
4,085 |
2 |
5K-CV |
T 80%: V 20% |
AC: 98.26%, P: 99.42%, R: 97.2%, F1: 98.19% |
Uses less paprameters of CNN and KNN that detects SARS-CoV-2 with good generability and less ovierfitting problem. |
Performance is bound to small dataset |
|
Basha et al. (2023)
|
CTs image |
Hybrid with neurosymbolic |
1,885 |
2 |
CV |
T 80%: V 20% |
AC: 98.7%, S: 99.8%, SP: 96.4% F1: 99.05% |
Two experiments investigate automated SARS-CoV-2 detection using NRCS and genetic-based methods, using neurotrophic logic. |
Time complexity should be reduced |
|
Pustokhin et al. (2023)
|
X-ray image |
Attention BiLSTM |
646 |
5 |
CV |
T 80%: V 20% |
S: 93.28%, SP: 94.61%, P: 94.90%, AC: 94.88%, F1: 93.10% |
RCAL-BiLSTM model uses bilateral filtering, feature extraction, and softmax classification for image classification. |
Real time implementation should be developed |
|
Deepak et al. (2023)
|
X-ray image |
Quantum neural network with RFNN |
3,871 |
4 |
CV |
T 80%: V 20% |
AC: 99.25% |
Hybrid median bilateral filter, SC-ResNet 50 segmentation, robust feature neural network extraction, DSFSAM, HWOA, and deep-QNN classify X-rays into multiple disease classes, reducing noise and enhancing infected regions. |
Study should explore optimization techniques for system loss reduction. |
|
Hu et al. (2022)
|
X ray |
Attention ResrNet |
4,449 |
3 |
CV |
T 80%: V 20% |
SN: 90.2% |
Attention fusion feature for automatic SARS-CoV-2 from CT images |
Result is biased to image segmentation |
|
Ismael & Şengür (2021)
|
X-ray |
CNN, SVM |
380 |
2 |
CV |
T 80%: V 20% |
AC: 94.7% |
Use SVM and CNN detect SARS-CoV-2 fastly |
Weak performance limited data |
|
Zhang et al. (2021)
|
X-ray |
Attention GAN |
100 |
2 |
CV |
T 80%: V 20% |
S: 69.85% P: 94.6% |
Hybrid model that performs good with some prepossessing |
Small data |
|
Wang et al. (2021b)
|
CT Scan |
FGCN |
320 |
2 |
CV |
T 80%: V 20% |
AC:97.71% |
Handles feature context efficiently |
It is not adaptable enough |
|
Islam et al. (2020)
|
X ray |
CNN-LSTM |
40,000 |
3 |
CV |
T 80%: V 20% |
AC: 99.4% S: 99.2% F1: 98.9% |
Hybrid but nicely handle features and classify it |
Need to be adaptable to analyse multi class data |
|
Shah et al. (2021)
|
X-ray |
CNN-BiGRU |
424 |
3 |
CV |
T 80%: V 20% |
AC: 96.00%, P: 96.00%, R: 96%, F1: 95% |
Relatively successful as a hybrid model in SARS-CoV-2 detection |
Data augmentation problem |
