Table 6.
Performance analysis of the proposed 3-class based model with relevant existing deep learning models in terms of accuracy and trainable parameters.
| Reference | Method | Data Size | Accuracy (%) | Trainable Parameters | ||
|---|---|---|---|---|---|---|
| COVID-19 | Normal | Pneumonia | ||||
| (Apostolopoulos & Mpesiana, 2020) | VGG19 | 224 | 504 | 714 | 93.48 | 143,667,240 |
| (Anunay Gupta et al., 2021) | Inception-V3 | 361 | 365 | 362 | 97.00 | 24,000,000 |
| (George et al., 2023) | GrayVIC model | 2,250 | 2,250 | 2,250 | 97.41 | 2,684,650 |
| (Hussain et al., 2021) | CoroDet | 500 | 800 | 800 | 94.20 | 2,874,635 |
| (Heidari et al., 2020) | VGG16 | 445 | 2,880 | 5,179 | 94.50 | 138,000,000 |
| (A. I. Khan et al., 2020) | CoroNet | 284 | 310 | 657 | 95.00 | 33,969,964 |
| (Nayak et al., 2023) | LW-CORONet | 2358 | 8066 | 5575 | 95.67 | 680,000 |
| (Ukwandu et al., 2022) | MobileNet-V2 | 1,200 | 1,341 | 1,345 | 94.50 | 3,538,984 |
| (L. Wang et al., 2020) | COVID-Net | 358 | 8,066 | 5,538 | 93.30 | 11,750,000 |
| (Zebin & Rezvy, 2021) | EfficientNetB0 | 202 | 300 | 300 | 96.80 | 5,300,000 |
| Proposed Model | Lightweight CNN Model | 3,616 | 10,192 | 1,345 | 96.83 | 592,929 |