Disease Classification of Liver and Lungs Using Deep learning

Project Code :TCMAPY958


The main objective of disease classification of liver and lungs using deep learning techniques is to develop accurate and efficient models that can automatically identify and classify diseases affecting the liver and lungs based on medical imaging data, aiding in early diagnosis, treatment planning, and patient care. By leveraging the power of deep learning algorithms, the aim is to achieve high-performance disease classification models that can assist healthcare professionals in making informed decisions and improving patient outcomes.


Lung disease is among the leading diseases that cause mortality worldwide. Most cases of lung diseases are detected when the disease is in the advanced stages. Therefore the development of systems and methods that enable faster and early diagnosis will play a vital role in the world today. Computer Aided Diagnosis (CADx) systems play such a role and are currently being expanded. This study explores the potential of using deep learning features from pre-trained deep learning architectures to provide rich and robust features. These features were compared to the conventionally used Gray-level Co-occurrence Matrix (GLCM). Deep features produced the highest accuracy of 100% as compared to 93.52% produced by using GLCM features. This study also compared the classification of deep features with five different classifiers and Support Vector Machine (SVM) showed the highest result. This high accuracy was also reproduced with Linear Discriminant Analysis (LDA) and Regression classifiers. Principal Component Analysis (PCA) was also done to evaluate the usage of reduced number of features and its effect on the classification performance.

Keyword: Medicinal, deep learning, Mobile net model, resnet, minutiae

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram



H/W Specifications:

Processor:I5/Intel Processor

RAM: 8GB (min)

Hard Disk: 128 GB

S/W Specifications:

Operating System: Windows 10

Server-side Script: Python 3.6

IDE: PyCharm,Jupyter notebook

Libraries Used: Numpy, IO, OS, Flask, keras, pandas, tensorflow,OpenCV, pytesseract OCR

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