In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold crossvalidation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.
<span id="docs-internal-guid-e57881bf-7fff-62db-2c1e-192664c8e8a8"><span>Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute hepatitis patients. This research uses data of hepatitis examination result with amount of 113 data and 5 features. The method that used is support vector machines (SVM) and random forest method. SVM is the classification method that uses discriminant hyper-plane, dividing to classes. meanwhile, random forest is a tree-based ensemble depending on a collection of random variables. SVM and random forest (RF) are applied to predict hepatitis data, and then the results will be compared.</span></span>
<span id="docs-internal-guid-2f1ba81b-7fff-8c46-5600-cbb159235091"><span>In the medical field, technology machinery is needed to solve several classification problems. Therefore, this research is useful to solve the problem of the medical field by using machine learning. This study discusses the classification of pancreatic cancer by using regression logistics and random forest. By comparing the accuracy, precision, recall (sensitivity), and F1-score of both methods, then we will know which method is better in classifying the pancreatic cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that random forest has better accuracy than logistic regressions. It can be seen with maximum accuracy of logistic regressions 96.48 with 30% data training and random forest 99.38% with 20% of data training.</span></span>
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