Deep Learning based Classification with Bio-medical Data

Deep learning (DL) is one of the state-of-the art techniques in practical and theoretical perspective. As a significant method to extract features, deep learning is applied to many fields like computer vision, data analysis, and others. Convolutional Neural Network (CNN), which is the popular DL architectures, has been successfully providing excellent features for some computer vision tasks. Based on a large volume and diversity of database, deep learning figure out key features and solve problems on classification, recognition, or reconstruction. Moreover, designing deep learning architectures as well as optimizers are popular and principal tendencies for enhancing the efficiency and performance of deep learning systems. In this project, we aim to solve one of the problematic issues of deep learning the imbalance database. Usually, large medical databases with high quality and quantity are difficult to collect and be publicly available. Hence, some techniques were investigated as alternative methods to strengthen deep learning applications which are restricted and have a low performance when training database is not ideal such as using non-medical database for chest pathology detection training [5],visual features map [26], neuroimaging data model [35], and others. Thus, in this project, we proposed two methods to analyze two specific well-liked kinds of medical database: Skin cancer database and electrocardiogram signal. To understand what good features are for skin cancer classification, we investigated image features of melanoma and benign images using VGG-19 that is a CNN model trained with the general ImageNet [50] database. Skin cancer data with labels (900 images) were obtained from 2016 IEEE ISBI challenge about Skin Lesion. These images were fed into VGG-19 [26] and each image yielded a vector with 1000 elements that are corresponding to classes in. These feature vectors were clustered into 8 categories using k-means algorithm with 3 different distance metrics: Euclidean distance, correlation, and cityblock distance (l1 norm). K-means algorithm with Euclidean distance yielded concentrated clusters that do not have any differentiation power. K-means with other metrics yielded histograms of clusters that are wellspread. Among them, k-means with cityblock yielded the best consistent images for each category (e.g. images with circular patch or pale orange background). Most of these clusters contain both melanoma and benign images, but one category contains mostly benign images (about 100 images). This implies that noncancer related background information may affect CNN for melanoma detection when this database is used without care. Additionally, we also proposed a specific method to solve the imbalance issue for signal classification. Biometrics due to the electrocardiogram(ECG) is one of the most promising techniques to substitute for classical methods such as iris, fingerprint, hand geometry, voice, or face. Though, the variations of ECG affect a lot on the performance of user identification. These variations derive from many multiple states (e.g exercises, stress, tension) as well as physiology changes of the heart. Thus, the proposed method is an augmentation method for QT interval correction (QTc augmentation). Aim to evaluate our investigated technique, we apply the model to publicly available ECG-ID database. The ECD-ID database with QTc augmentation is identified by several machine learning methods such as SVM with different kernels parameters, deep learning architectures (e.g RNN, CNN). From that, we evaluated the different performances of classifier techniques with and without QTc augmentation.