A semi automatic classification of Lung disease using CNN
KRISHNAVENI T
, PRABADEVI P
CT , LBP, CVH, CNN, Genetic algorithm
CT images were used to identify the lung disease. It is used to observe a variety of lung texture patterns. These images are a mixture of different patterns and therefore it becomes very difficult for the radiologist to distinguish between them and diagnose the disease. One way to solve this problem is to use Convolutional Neural Networks (CNN) have been applied in the field of medical imaging research and have successfully demonstrated their ability for image classification and recognition. Classifying the medical images by selecting the optimal features improves the performance of the classification process. Choosing the best features reduces the time and algorithm effort of the overall process. The main objective of the process is to select the optimal features from the different types of features extracted using different methods. Features were extracted from the images based on LBP features and CVH features. To use the best fit function based on Fisher's criterion to select the optimal features. Combining the fitness function with genetic optimization to improve the efficiency of the genetic algorithm. Feature extraction, optimization and classification play the efficient role to improve accuracy. The overall performance of was measured using performance metrics such as accuracy, sensitivity and specificity.
"A semi automatic classification of Lung disease using CNN", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.a290-a293, March-2023, Available :https://ijnrd.org/papers/IJNRD2303028.pdf
Volume 8
Issue 3,
March-2023
Pages : a290-a293
Paper Reg. ID: IJNRD_188168
Published Paper Id: IJNRD2303028
Downloads: 000118836
Research Area: Computer Science & Technology
Country: kovilpatti, tamilnadu, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
