Table 2.5 Models with hardware used and time required for training.
| Ref. | Dataset | Hardware and software platform used | Input image size | Time required for training |
|---|---|---|---|---|
| [47] | CheXpert | NVIDIA Geforce RTX 2080 Ti with 11GB memory. Python with Keras and TensorFlow | 224 × 224 pixels | - |
| [51] | Lung ultrasonography videos from Italy | RTX-2080 NVIDIA GPU | 1,005 frames | 11 hours |
| [46] | NIH Tuberculosis Chest X-ray dataset [18] and Belarus Tuberculosis Portal dataset [21] | Nvidia GeForce GTX 1050 Ti | 512 × 512 | 5–6 ms |
| [26] | ChestX-ray14 dataset | 8-core CPU and four TITAN V GPUs Pytorch 1.0 framework in Python 3.6 on an Ubuntu 16.04 server | 224 × 224 | - |
| [20] | ChestX-ray14 dataset | NVIDIA TITAN Xp GPUs Pytorch | 224 × 224 | 6 hours |
| [70] | ChestX-ray14 dataset | Dev-Box linux server with 4 Titan X GPUs | 224 × 224 | - |
| [5] | ChestX-ray14 dataset | Intel Core(TM) i7-6850k CPU 3.60GHz processor, 4TB of hard disk space, 7889 MB of RAM, and a CUDA-enabled NVidia Titan 11 GB graphics processing unit with python and Keras library on TensorFlow | 224 × 224 | - |
| [49] | ChestX-ray8 | NVIDIA GeForce GTX TITAN and PyTorch | 512 × 512 | 20 hours |
| [46] | NIH Tuberculosis Chest X-ray [18], Belarus Tuberculosis [A6] | Nvidia GeForce GTX 1050 Ti | 512 × 512 | 1 hour |
| [61] | Kaggle PSNA | Nvidia Tesla V100 and Nvidia K80 and Keras library of Python | 512 × 512 | 7 hours |
| [13] | St. Michael’s Hospital chest x-ray | 3 NVIDIA Titan X 12GB GPUs | 256 × 256 | 1 hour |
| [35] | NIH Tuberculosis Chest X-ray [18], Belarus Tuberculosis [A6] |
Intel i5
|