At the end of 2019, a new disease called Coronavirus Disease (COVID-19) originated in Wuhan, China. This disease is caused by respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, there are tests to detect COVID-19, such as PCR and Rapid Test. This detector takes a long time and is less accurate in producing a diagnosis. This study aims to classify chest X-ray images using CNN and Transfer Learning methods to diagnose COVID-19. The proposed model has 4 scenarios: CNN Handcraft Model, Transfer Learning (VGG 16, VGG 19, and ResNet 50). This model is accompanied by data augmentation and data balancing techniques using undersampling techniques. The dataset used in this study is the “Covid-19 (COVID-19 and Normal) Radiographic Database” with 13,808 data divided into two classes, namely COVID-19 and Normal. Each model built will produce values for accuracy, precision, recall, and confusion matrix. The results of CNN Scenario 1 accuracy is 95%, in Scenario 2 VGG 16 the accuracy is 93%, Scenario 3 VGG 19 is 90% and Scenario 4 ResNet 50 is 80%.

Brain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher applied.

Alzheimer's disease is the most common cause of dementia. Dementia refers to brain symptoms such as memory loss, difficulty thinking and problem solving and even speaking. This stage of development of neuropsychiatric symptoms is usually examined using magnetic resonance images (MRI) of the brain. The detection of Alzheimer's disease from data such as MRI using machine learning has been the subject of research in recent years. This technology has facilitated the work of medical experts and accelerated the medical process. In this study we target the classification of Alzheimer's disease images using convolutional neural network (CNN) and transfer learning (VGG16 and VGG19). The objective of this study is to classify Alzheimer's disease images into four classes that are recognized by medical experts and the results of this study are several evaluation metrics. Through experiments conducted on the dataset, this research has proven that the algorithm used is able to classify MRI of Alzheimer's disease into four classes known to medical experts. The accuracy of the first CNN model is 75.01%, the second VGG16 model is 80.10% and the third VGG19 model is 80.28%.