systems br Khosravia et al Deep convolutional
Khosravia et al.  2018 Deep convolutional neural Classifying the various Increase the precision of Computation cost was not
networks (CNN) cancer tissues diagnosis and minimizes minimized
Sharma et al.  2018 Two-stage hybrid ensemble Classifying the chronic Accurate diagnosis of the The multi-stage diagnosis
technique kidney disease disease with a feature set was not performed with
Rabbani et al.  2018 Machine learning (ML) Extracting and analyzing Improves diagnosis, The ML algorithms used for
method Combining artificial several quantitative features treatment and outcomes feature extraction was not
intelligence approaches from medical images
attained the accurate results
Baranidharan et al. . 2016 Image-based features Classify the lung cancer Increase the true positive Error rate was not
selection method images rate effectively minimized
Proposed – Weight Optimized Neural Lung Cancer Disease Increase diagnosing –
Network with Maximum diagnosis with big data accuracy and minimizes the
false positive rate,
applied to both original and newly created database to predict the lung cancer type of tumors which results in improved accuracy. However, the model reduces the processing time, but the false positive rate was not minimized. Evaluation of ML algorithm for lung cancer diagnosis was carried out by Podolsky et al. . It accurately predicts cancer vulnerability as well as minimizes the false positive rate. But, the classification time was not exploited which can be helpful for early lung cancer detection.
A narrative review based on radiomic features to help diag-nose lung cancer in an early stage was proposed by Rabbani et al. , where the ML algorithms were combined with artificial intelligence approaches. The objective of radiomics remains in ex-tracting and analyzing several quantitative features from medical images. Moreover, they JNJ42153605 focused on highly promising in stag-ing, diagnosing, and predicting outcomes of cancer treatments. However, the machine learning algorithms used, but the feature extraction does not provide accurate results. Zhou et al. 
proposed a multi-modality and multi-classifier radiomics predic-tive models to address the aforementioned issues using a new reliable classifier fusion strategy. Here, the training of modality-specific classifiers was first made, followed by an analytic eviden-tial reasoning (ER) rule, which was used to combine the output score from each modality to build an optimal predictive model towards disease diagnosis. This model failed to minimize the disease prediction time.
A systematic review of mortalities and survival rate of lung cancer with evolutionary algorithms was conducted by Dubey et al.  to identify a better method for early lung cancer diagnosis and to achieve higher accuracy rate with deep learning techniques. It does not minimize the error rate. Liu et al.  proposed a MultiView Convolutional Neural Networks (MV-CNN) for efficient lung nodule classification, to improve the accuracy, and the classification time. Here, accurate detection was not performed with the features. Baz et al.  explored some crucial challenges and methodologies with CAD system for lung cancer. It increases the detection and diagnosis of lung nodules, but the accurate feature selection was not performed to minimize the detection time.
Deep feature fusion and hand-crafted features for lung nodule classification was developed by Wang et al. . But, classifi-cation performance was not accurate. CAD was introduced for enhancing the performance of nodule candidate classification by Chen et al. . However, classification time was not minimized. In order to effectively classify the lung nodules, deep features were extracted in CT images with higher accuracy by Kumar et al. . But, the error rate was remained unaddressed. Image-based features selection method was developed for classifying the lung cancer images with higher accuracy Baranidharan et al. . In coronary arteries method, novel fusion-based selection was used to select the features for classification. During the feature selection, the redundant features were unable to be removed thus introduced an error in classification process. To overcome this problem, the proposed WONN-MLB method used Newton–Raphson’s Max-imum Likelihood mode, where MLMR are used to choose the most relevant attributes. Then, the boosting classifier is applied to classify the attributes for LCD diagnosis, which reduces the error rate in the classification process.