Error code:   ConfigNamesError
Exception:    ValueError
Message:      Feature type 'Jpg' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image']
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1889, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1238, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 464, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 395, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1921, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1761, in from_dict
                  obj = generate_from_dict(dic)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1402, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1402, in <dictcomp>
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1408, in generate_from_dict
                  raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
              ValueError: Feature type 'Jpg' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image']

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Project Overview Tuberculosis (TB) remains a major public health challenge, especially in rural India, which accounts for 26% of the global TB burden. Limited healthcare access, a shortage of medical professionals, and high diagnostic costs exacerbate the issue. This project aims to address the delayed detection of TB in rural India using AI-based chest X-ray analysis, enabling early detection and treatment.

Key Problems 1. Diagnostic Gaps: Lack of access to quick, accurate TB screening in rural areas. 2. Resource Constraints: Shortage of trained radiologists. 3. Inconsistent Imaging Quality: Variable X-ray quality from different machines. 4. Scalability Challenges: Difficulty scaling traditional screening methods. 5. Integration Issues: Working within existing healthcare infrastructure.

Solution Approach Develop an AI-based system for early TB detection using chest X-rays, optimized for mobile devices and designed for use by minimally trained healthcare workers in rural areas.

Key Components: 1. Deep Learning Model: For detecting TB with high sensitivity and specificity. 2. Mobile Application: Optimized for use offline on smartphones/tablets. 3. Scalability: System deployment in rural health centers. 4. Training Program: For rural healthcare workers to use the system effectively.

Project Goal 1. Model Development: Create a deep learning model for TB detection with 90% sensitivity and specificity. 2. Mobile App: Build an offline-capable mobile app for use in rural areas. 3. Deployment: Implement in 50 rural health centers across 3 states in India. 4. Time Reduction: Decrease TB diagnosis time by 50% in targeted areas.

Expected Outcomes 1. Validated AI Model for TB detection optimized for mobile deployment. 2. Training Program for healthcare workers on the AI system. 3. Database of anonymized chest X-rays for ongoing research. 4. Published Research on model development and real-world performance.

Learners' Contributions Data Collection & Preprocessing Gather diverse datasets from rural India, implement data augmentation, and ensure data anonymization.

Model Development Explore deep learning architectures (e.g., CNNs, Vision Transformers) and employ transfer learning techniques. Model Optimization & Mobile Deployment Optimize for mobile use with model pruning and quantization techniques for offline deployment. User Interface Development Design an intuitive interface for healthcare workers with minimal technical training. Validation & Testing Conduct rigorous testing and user acceptance trials with rural healthcare workers.

Impact Assessment

Health Impact: 40-50% increase in early-stage TB detection. 30-35% improvement in treatment success rates.
Healthcare System Impact: 50-60% reduction in time to diagnosis. 70-80% increase in rural healthcare workers' capability to conduct TB screenings.
Technological Impact:

Increased AI adoption in rural healthcare and better digital health record management.

Social Impact:

Increased health-seeking behavior and TB awareness in target communities.

Beneficiaries:

TB patients, families of TB patients, the Indian healthcare system.

Conclusion This project seeks to bridge the diagnostic gaps in TB detection in rural India by leveraging AI and mobile technology, empowering healthcare workers and improving TB detection and treatment outcomes.

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