Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label fyp-dataset@640d49fc9c51643b4e8c83657c395d25889c8627
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label fyp-dataset@640d49fc9c51643b4e8c83657c395d25889c8627

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FYP Dataset

Medical imaging datasets used in the FYP / Radisist project. Mirrored here for reproducibility.

covid19_radiography/ — COVID-19 Radiography Database

Source: Kaggle tawsifurrahman/covid19-radiography-database (CC BY-NC-SA 4.0).

Chest X-ray images with paired lung-region segmentation masks.

Folder layout

covid19_radiography/
├── COVID/            (images/ + masks/)
├── Lung_Opacity/     (images/ + masks/)
├── Normal/           (images/ + masks/)
└── Viral Pneumonia/  (images/ + masks/)
  • images/*.png — 299x299 grayscale chest X-rays
  • masks/*.png — 256x256 binary (0/255) lung-region masks

Class sizes (original)

Class Images
COVID 3,616
Lung_Opacity 6,012
Normal 10,192
Viral Pneumonia 1,345
Total 21,165

Notes

  • Image filename matches its mask filename.
  • For the trained models (4-class DenseNet121 classification @ 96.17% accuracy; UNet lung segmentation @ 98.71% Dice), near-duplicates were removed via MD5 + perceptual pHash (mirror-aware), leaving 19,133 unique images, split stratified 70/15/15 with no train/test leakage. See the model cards at umairinayat/medical-models (disease_models/chest_xray/).

Citation

M.E.H. Chowdhury, T. Rahman, A. Khandakar, et al. "Can AI help in screening
Viral and COVID-19 pneumonia using X-ray?." Computers in Biology and Medicine, 2020.
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