ILD Image Classification and Cancer Prediction using Machine Learning
Yuva Teja Kadari, Srujan Nalam, Aleemuddin Mohd, Dr. Nanda Kumar M
Convolution Neural Network (CNN), Computer Aided Detection (CAD), High Resolution Computed Tomography (HRCT), Computed Tomography (CT), quantitative analysis, Pixel Mapping, Medical Imaging technologies
In recent years, medical image technology has advanced rapidly and x-rays, ultrasound (US), MRI scans, and CT scans have become commonly used methods for diagnosing human illnesses. Among these techniques, CT scans provide the highest resolution images. As a result, High-Resolution CT (HRCT) was created, which is an innovative CT technology that collects images in three dimensions (3D). This improves image quality by increasing resolution through the use of spatial pixels, leading to improved scan rate and reduced pixel size. Images from HRCT may reveal symptoms and assist in differential diagnosis by providing visual patterns. However, the diagnosis's precision and accuracy are contingent upon the knowledge of doctors, radiologists, or pathologists. Unfortunately, misjudgments can result in incorrect treatments or diagnoses. To address these issues, there has been a move towards using computer-based technology instead of manual operations. This shift has been made to improve efficiency, accuracy, and consistency. The technology is achieved through transfer learning methods (CNN) and Computer Aided Detection (CAD). Medical imaging technology has made significant progress in recent years, with HRCT being a cutting-edge development that provides clearer images and assists in diagnosis. The shift towards computer-based technology in medical imaging is aimed at improving efficiency, accuracy, and consistency in diagnoses.
Article Details
Unique Paper ID: 161024

Publication Volume & Issue: Volume 10, Issue 2

Page(s): 490 - 497
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