br A Critical assessment of
A Critical assessment of ANN was carried out in Dande et al. [10] which results in an increase in the efficacy and specificity of the diagnostic techniques, but it fails to minimize the computa-tional complexity. Tumor tissue based on pathological evaluation is considered to be one of the most pivotal for early diagnosis in cancer patients. However, the automated image analysis methods have the potential to improve the accuracy of disease diagnosis and to minimize human errors. Khosravia et al. [11] proposed different computational methods using convolutional neural net-works (CNN), where a stand-alone pipeline was constructed in an effective manner to classify several histopathology images across different types of cancer. But, it fails to minimize the computation cost while classifying the various types of cancer.
Sharma et al. [18] proposed a two-stage hybrid ensemble clas-sification technique to increase the prediction accuracy of chronic kidney disease with ML technique. It improves the disease diag-nosis, but the multistage classification was not performed with minimum time. Early diagnoses of lung cancers and differentia-tion between the tumor types and non-tumor types have been required to improve the patient survival rate. In Hosseinzadeh et al. [13], a diagnostic system with structural and physicochemi-cal attributes of proteins via feature extraction, feature selection, and prediction models was designed. Then, the ML models were
Table 1
Comparison of the proposed approach with the state-of-the-art approaches.
Author
Year
Approach
Objective
Pros
Cons
Das et al. [4]
2010
Ensemble learning methods
To classify the Valvular
Minimize the classification
Classification accuracy rate
heart disease
time
remained unsolved
Ghorai et al. [3]
2011
Nonparallel Plane Proximal
Perform cancer classification
Provides better classification
Valvular WY-14643 disorders
Classifier (NPPC)
with higher accuracy in a
accuracy with lesser
classification was difficult
Computer Aided Diagnosis
computation time
Baz et al. [12]
2012
Computer-aided diagnosis
Lung cancer diagnosis
Achieve better detection
Accurate feature selection
(CAD) system
and diagnosis of lung
was not performed
nodules
Hosseinzadeh et al. [13]
2013
Machine learning models
Predict and detect the type
Provide more accurate
The false positive rate was
of lung tumors
results in lung tumor
not minimized
detection
Adetiba et al. [8]
2015
Radial Basis Function Neural
Classifies the Lung Cancer
Improve classification,
Performance of feature
Network with Affine
accuracy and achieves, and
extraction was not
Transforms
low mean square error
improved
Kumar et al. [14]
2016
Evolutionary algorithms
Lung cancer detection
Detect the lung cancer
The error rate was not
accurately with minimum
minimized
time
Huda et al. [6]
2016
Global optimization based
Tumor classification with
Increases the imbalanced
Classification time was not
hybrid wrapper-filter
the imbalanced healthcare
healthcare data
minimized
feature selection with
data
classification
ensemble classification
Podolsky et al. [15]
2016
Machine learning algorithm
Lung cancer diagnosis
Increase the accuracy of
Classification time remained
predicting cancer
unsolved
susceptibility and Minimize
false positive
Zhou et al. [16]
2017
Multi-modality and
Extract numbers of
Increase the disease
Failed to minimize the
multi-classifier radiomics
quantitative features and
prediction accuracy with
disease prediction time.
predictive models
disease prediction
the features
Kang et al. [17]
2017
Multi-view convolutional
lung nodule classification
Increases the classification
Failed to attain accurate
neural networks (MV-CNN)
accuracy and minimizes the
disease prediction with
time
features
Dande et al. [10]
2017
Artificial Neural Network
Diagnosis and evaluation of
Increase the efficacy and
Failed to minimize the
medical conditions
specificity of disease
computational complexity
diagnosis
Costaa et al. [5]
2018
Generalized mixture (GM)
Increase the classification
Handles single-label
Multi-label classification
functions
accuracy of a stability classification
classification problems
problem remained
system
unaddressed
Hussein et al. [7]
2018
Proportion-Support Vector
Categorizes the Lung
Improve the diagnosing
Failed to minimize the error
Machine (SVM)
Nodules
accuracy
rate
Jain et al. [9]
2018
Feature selection and
Enhancing the accuracy of
Classification systems for
The classification time was
parallel classification
classification systems
effective disease prediction
not minimized