Title:Chest CT Image based Lung Disease Classification – A Review
Volume: 20
Author(s): Shri Ramtej Kondamuri*, Venkata Sainath Gupta Thadikemalla, Gunnam Suryanarayana, Chandran Karthik, Vanga Siva Reddy, V. Bhuvana Sahithi, Y. Anitha, V. Yogitha and P. Reshma Valli
Affiliation:
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
Keywords:
Computed tomography, Lung cancer, Pneumonia, Pulmonary embolism, Machine learning, Convolutional neural networks.
Abstract: Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's
respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis.
Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced
systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and
their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures
for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview
of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in
ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep
learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with
the awareness they require in order to recognize and classify certain medical disorders.
