Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

Table 8

Comparison with state-of-the-art studies.

Serial no.	Ref	Title	Methods	Accuracy (%)	Specificity (%)	Sensitivity (%)
1	[48]	Artificial neural network-based classification of lung nodules in CT images using intensity, shape, and texture features	ANN with texture, shape and intensity features	93.2	91.3	93.1
2	[16]	Multimodel ensemble learning architecture based on CNN for lung nodule malignancy suspiciousness classification	CNN-based multimodal framework (VGGNet, InsepNet, ResNet)	94	93.9	83.7
3	[49]	A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework	DL-based convolutional neural network	96.33%	NA	96.37
4	Proposed	Identification and classification of lungs focal opacity using CNN segmentation and optimal feature selection	CCN with geometric, HOG, LBP features and SVM classifier	97.8	93.3	100