CONVOLUTIONAL NEURAL NETWORK BASED METHOD FOR DETECTING LUNG DISEASE

Abstract

ABSTRACT A convolutional neural network (CNN) based method (100) for detecting lung disease, the method (100) comprising the steps of inputting medical images data of a patient’s lungs, pre-processing the medical images data, training a convolutional neural network model using patterns indicative of lung diseases to generate a disease output, predicting a lung disease based on the pre-processed medical images data and the disease output and generating a report indicative of the disease output.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of lung disease and in particular relates to a method for convolutional neural network (CNN) based method for detecting lung disease.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Lung disease, which encompasses conditions such as pneumonia, chronic obstructive pulmonary disease (COPD), lung cancer, and interstitial lung disease, presents significant diagnostic challenges due to the complex visual characteristics these diseases exhibi , Claims:1. A convolutional neural network (CNN) based method (100) for detecting lung disease, the method (100) comprising the steps of:
inputting medical images data of a patient's lungs;
pre-processing the medical images data;
training a convolutional neural network model using patterns indicative of lung diseases to generate a disease output;
predicting a lung disease based on the pre-processed medical images data and the disease output, and
generating a report indicative of the disease output.

2. The method (100) as claimed in claim 1, further comprising:
receiving symptom data of the patient;
analysing the symptom data using a random forest model based at least on the disease output; and
generating the report indicative of the disease output based at least on the analysed symptom data.

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