{'fraction_non_alphanumeric': 0.02108433734939759, 'fraction_numerical': 0.0, 'mean_word_length': 5.042424242424242, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
ChestXthon: An algorithm for Abnormality Detection in Chest Radiographs

Chest illnesses like heart failure, lung tumor or lung tuberculosis, and so on is frequently in view of chest X-ray images (CXR). The ailments are treatable on the off chance that they are recognized in their beginning times. Analyzing CXR is a tedious procedure. Now and again, therapeutic specialists had ignored the illnesses in their first examinations on CXR, and when the pictures were reevaluated, the malady signs could be detected. Furthermore, the quantity of CXR to look at is various and a long ways past the capacity of accessible therapeutic staff, particularly in creating nations. A PC supported finding (CAD) framework can check presumed zones on CXR for cautious examination by restorative specialists, and can give caution in the cases that need critical consideration. This paper reports our persistent work on developing an algorithm that aids the radiologists for the diagnosis of chest radiographs.  