Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the CT scan of a person to be in the training set, while other images of the exact same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat-map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.Comment: This version has been submitted to CAAI Transactions on Intelligence Technology. 202

A STATISTICAL FEATURE EXTRACTION IN WAVELET DOMAIN FOR MOVEMENT CLASSIFICATION: A CASE STUDY FOR EYES OPEN, EYES CLOSED, AND OPEN/CLOSED FIST TASKS

DergiPark: 498001ejovocAnalysis of brain signals constitute an importance,especially for paralyzed people or people suffer from motor disabilities. Forthis aim, some studies have been evaluated to measure signals from the scalp toprovide non-muscle control arguments. Brain-Computer Interface Systems turnsthese signals into device signals that are controllable at the level ofthought. In this paper, we classify diverse tasks according to EEG(electroencephalogram) signals. Then pre-processing, feature extraction andclassification steps are hold. For classification, we use FLDA, Linear SVM,Quadratic SVM, PCA, and k-NN methods. The best result is obtained by usingk-NN

THE USAGE OF STATISTICAL FEATURES IN THE APPROXIMATION COMPONENTS OF WAVELET DECOMPOSITION FOR ECG CLASSIFICATION: A CASE STUDY FOR STANDING, WALKING AND SINGLE JUMP CONDITIONS

DergiPark: 498009ejovocThe purpose of this study is to classifyelectrocardiogram (ECG) signals with a high accuracy rate. The ECG signals usedare obtained from the Physiobank archive. These signals are preprocessed toremove noise. Features with distinctiveness in classification are obtained bothin the time domain and the frequency domain. The Discrete Wavelet Transformmethod is used for feature extraction in frequency domain. ECG signals areclassified by the Naive Bayes method after the required features are extracted

Effect of heat curing with electrical resistance on the concrete properties

Soğuk havalarda beton karma suyunun donması betonda dayanım kaybına ve geçirimliliğe neden olmaktadır. Ayrıca soğuk hava koşullarında beton prizini oldukça yavaş alır. Bu çalışmada, soğuk havada hızlı priz alan beton üretilmesi hedeflenmiştir. Bu amaçla elektriksel direnç kullanılarak beton içeriden ısıtılmış ve kısa sürede betonun prizini tamamlaması sağlanmıştır. Isıtma için kullanılan kablo farklı boy ve farklı şekillerde kalıp içerisine yerleştirilmiştir. Hazırlanan beton karışımı bu kalıplara yerleştirilerek derin dondurucuya konulmuştur. Derin dondurucuda -15, -20, -25 derece ortam sıcaklıklarında priz alıncaya kadar bekletilmiştir. Betonun priz alma sürecindeki sıcaklığı her 30 dakikada bir ölçülmüştür. Bu yöntemle soğuk hava koşullarına rağmen 4,5 saat gibi kısa bir sürede beton priz almıştır. Kalıptan çıkarılan numuneler kür havuzuna bırakılmıştır. 7. ve 28. günde numunelerden kesilerek alınan 8 cm boyutlarındaki küp numuneler üzerinde yapılan deneyler sonucunda betonun birim hacim ağırlığı, basınç dayanımı, su emme, ultrases geçiş hızları hesaplanmıştır. Oldukça düşük sıcaklıklarda elektrik direnci ile ısıtma sayesinde betonun prizini tamamlamasının beton özelikleri üzerinde olumlu etkileri gözlemlenmiştir.Freezing of the concrete mix water in cold weather caused strength loss of concrete and permeability. Besides, the setting of concrete is accomplished slowly in the cold weather conditions. In this study, the main goal is the production of quickly setting concrete in a cold weather. For this purpose, the inner side of a concrete part is heated by an electrical resistance and therefore the setting of concrete is completed in a short while by this way. The cable for heating purpose is placed with different lengths and shapes in the template. Prepared concrete mix is placed this templates in deep freeze. They are waited until setting period of concrete in deep freeze at -15, -20, -25 degrees ambient temperature. The concrete temperature during the setting period of concrete is measured. The concrete is set in a short time such as 4.5 hours though by using this method in the cold weather conditions. Samples were removed from the mold and left to cure pool. After having experiments on 8 cm cube samples cut form the samples day 7th and 28th day, the unit weight of concrete, compressive strength, water absorption, ultrasonic pulse velocity are calculated. Completion of concrete setting through heating electrical resistance at very low temperatures has had a positive impact on the concrete characteristics

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis