A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network

Sarhan, Ahmad M.

doi:10.4236/jbise.2020.135008

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…Finally, when the proposed system and 69 are compared, it can be seen that the proposed system is better than the results obtained using deep learning architectures. In addition, it can be seen from Table 6 that our study results for other data collections give good enough and/or consistent results 61–66 . Indeed, there is also no GUI application designed in these studies.…”

Section: Resultsmentioning

confidence: 61%

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Lung cancer detection by using probabilistic majority voting and optimization techniques

Sünnetci

Alkan

2022

Int J Imaging Syst Tech

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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 (%), F 1 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.

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Section: Resultsmentioning

confidence: 61%

“…In addition, it can be seen from Table 6 that our study results for other data collections give good enough and/or consistent results. [61][62][63][64][65][66] Indeed, there is also no GUI application designed in these studies. Thus, this study is innovative and competitive both in terms of performance metrics and software.…”

Section: Resultsmentioning

confidence: 99%

Lung cancer detection by using probabilistic majority voting and optimization techniques

Sünnetci

Alkan

2022

Int J Imaging Syst Tech

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“…Figure 4 is the CNN architecture used in this research. [44]. In addition, the implementation of wavelet transform supports feature enhancement, thereby increasing the performance of the CNN model [45].…”

Section: Cnnmentioning

confidence: 99%

“…The classification layer consisted of a flattened process, a fully connected layer with several neurons of 256, sigmoid activation, and a dropout layer. Wavelet Transform is used before entering the CNN model even though there is feature extraction in the layer because Wavelet Transform can simplify the work of the CNN model due to wavelet decomposition[44]. In addition, the implementation of wavelet transform supports feature enhancement, thereby increasing the performance of the CNN model[45].…”

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confidence: 99%

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Saraswati,

Dewi,

Pirozmand

2024

LKJITI

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Recognizing pneumonia sufferers can be done by analyzing chest X-ray images. Pneumonia sufferers experience pleural effusion, where fluid is between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy or hazy. It differs from the appearance of X-rays on normal lungs which are dark in color. These differences in X-Ray images can be classified automatically with the help of Artificial Intelligence This research used convolutional neural networks and support vector machine methods to recognize X-ray images of pneumonia. This research applied Principal Component Analysis and Wavelet Transformation support to both methods. This research aimed to evaluate the performance of each model combination. The PCA-SVM model gave the best performance, with an accuracy of 94.545% and an F1 score of 94.675%. The SVM model outperforms the CNN model in recognizing images; in this case, it could be due to the relatively small amount of training data.

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“…However, detection techniques using genetic analysis tend to be high cost. Some other studies use traditional machine learning by performing feature extraction to get a feature vector which is then trained and validated as reported in [12]- [14]. Nevertheless, traditional machine learning will be time consuming if applied to large datasets, besides variations in image background, image source, size and color of images can reduce accuracy.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Convolution Neural Network Architecture for Colon Cancer Classification

2022

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In 2021, colon cancer is the second most common cause of death for this type of cancer. Therefore, in this study, a colon cancer classification system was developed to help medical staff classify 2 types of cancer colon adenocarcinomas and benign colonic tissues. The classification method uses the Convolution Neural Network (CNN) with the architecture VGG16, VGG19, ResNet101, ResNet152, MobileNetV2, DenseNet201 and InceptionV3. We used 10.000 image datasets that divided into 7200 training data, 1800 validation data and 1000 test data. Pre-trained models are used to extract new features and training data. The best performance parameter based on accuracy, precision, recall and f1-score and confusion matrix are obtained in 3 architectures, namely VGG19, ResNet101 and ResNet152. These architectures can identify and classify both types of colon cancer with 100% accuracy.

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A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network

Cited by 10 publications

References 43 publications

Lung cancer detection by using probabilistic majority voting and optimization techniques

Lung cancer detection by using probabilistic majority voting and optimization techniques

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Comparison of Convolution Neural Network Architecture for Colon Cancer Classification

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