Browsing by Subject "Learning models"

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    A Deep Learning Based Prediction Model for Diagnosing Diseases with Similar Symptoms
    (Institute of Electrical and Electronics Engineers Inc., 2021) Aygun I.; Kaya B.
    Diagnosis of diseases with similar symptoms may cause medical errors depending on the transfer of patient complaints. In this study, diseases that are similar to each other in terms of symptoms are primarily examined. In conducted experiments Diabetis Mellitus was the focus of the study and most similar disaeses to Diabetis Mellitus were determined by using statistical data and deep learning methods. Within the scope of the study, a data set containing the symptoms of patients with this disease was created. In experiments using the data of 205 patients, it was seen that the deep learning model produced the same diagnosis with physicians with a rate of over 84%. For nearly 10% of the patients used in the experiment, it was concluded that an alternative disease should also be checked. © 2021 IEEE.
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    Effects of Data Augmentation Techniques on Classification Performance in Knee MRIs
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kucuk E.N.; Gur A.
    The role of the amount of data used in increasing the effectiveness of deep learning models is very important. Due to the insufficient publicly available data in some sub-fields of health, data augmentation is vital. This study proposes an approach to improve the experimental work process in deep learning-based injury detection using data augmentation techniques on knee Magnetic Resonance (MR) images. The study is also one of the few in the body of literature to examine the impact of data augmentation in hard tissues. The effect of data augmentation on classification performance is tested using various transfer learning models, and the highest success rates in this study are determined for three classes. Forecasting achievements: the accuracy is 89.98% in the abnormal, 80.35% in the anterior cruciate ligament, and 76.66% in the meniscus classes. As a result of the experiments, it has been seen that the AutoAugment architecture works faster and generally gives more successful results than other augmentation methods. © 2023 IEEE.
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    An embedded TensorFlow lite model for classification of chip images with respect to chip morphology depending on varying feed
    (Springer, 2024) Özçevik Y.; Sönmez F.
    Turning is one of the fundamental machining processes used to produce superior machine parts. It is critical to manage the machining conditions to maintain the desired properties of the final product. Chip morphology and chip control are crucial factors to be monitored. In particular, the selection of an appropriate feed has one of the most significant effects. On the other hand, machine learning is an advanced approach that is continuously evolving and helping many industries. Moreover, mobile applications with learning models have been deployed in the field, recently. Taking these motivations into account, in this study, we propose a practical mobile application that includes an embedded learning model to provide chip classification based on chip morphology. For this purpose, a dataset of chips with different morphological properties is obtained and manually labeled according to ISO 3685 standards by using 20 different feeds on AISI 4140 material. Accordingly, TensorFlow Lite is used to train a learning model, and the model is embedded into a real-time Android mobile application. Eventually, the final software is evaluated through experiments conducted on the dataset and in the field, respectively. According to the evaluation results, it can be stated that the learning model is able to predict chip morphology with a test accuracy of 85.4%. Moreover, the findings obtained from the real-time mobile application satisfy the success rate by practical usage. As a result, it can be concluded that such attempts can be utilized in the turning process to adjust the relevant feed conditions. © The Author(s) 2024.