Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model

Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization

Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures.

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Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images

Combination of political optimizer, particle swarm optimizer, and convolutional neural network for brain tumor detection

The number of people dying from lung cancer in the world is increasing day by day. Therefore, early diagnosis of lung cancer holds a prominent position for the recovery of patients. Chest computed tomography scan images that are clinical adjuncts are used in the diagnosis of lung cancer. Herein, the proposed method automatically diagnoses lung cancer using machine learning algorithms, probabilistic majority voting, and optimization techniques. The paper uses images from Adenocarcinoma Left Lower Lobe, Large Cell Carcinoma Left Hilum, Normal, and Squamous Cell Carcinoma Left Hilum subjects. 80% and 20% of these images are used in the training and validation set, respectively, and there are 1000 images in the data set. For each image used in the study, image features are extracted using the Bag of Features method (2000 features). Linear Discriminant, Optimizable Support Vector Machine, and Optimizable K‐Nearest Neighbor (KNN) classifiers are trained using these features. Afterward, the new image label is estimated in two steps. First, if the two or three classifier outputs are the same, the image label is detected using the majority voting technique. Second, if the classifier outputs are not the same, the image label is determined according to the most successful classifier, Optimizable KNN. To better experience the majority voting technique used in the proposed method, its detailed theoretical framework is given in the study. Additionally, the Graphical User Interface application of the proposed method has been designed to save time and reduce the workload of expert radiologists. From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F1 Score (%), Matthews Correlation Coefficient, and Kappa of the proposed method are equal to 99.2837, 0.007163, 99.2571, 99.9139, 99.3486, 99.2816, 0.98738, and 0.94965 respectively.

Lung cancer detection by using probabilistic majority voting and optimization techniques

This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well‐organized classifiers, the convolutional neural network (CNN) and feature‐based methodology, have been used. In the first step, the CNN classifier is optimized using a newly designed optimization method called the improved Harris hawk optimizer. This method is applied to the dataset, and the classification is commenced. If the disease cannot be detected via this method, the results are conveyed to the second classifier, that is, the feature‐based method. This classifier, including Haralick and LBP features, is subsequently applied to the received dataset from the CNN classifier. Finally, if the feature‐based method also does not detect cancer, the case study is healthy; otherwise, the case study is cancerous.

Zhiqiang Guo

Papers

Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics