Comparing stacking ensemble techniques to improve musculoskeletal fracture image classificationKandel, Ibrahem (Avtor) Castelli, Mauro (Avtor) Popovič, Aleš (Avtor) neurosciencedeep learningimage classificationstackingensemble learningconvolutional neural networkstransfer learningmedical imagesBone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks' performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.20212021-06-22 11:32:02Članek v reviji127770UDK: 659.2:004ISSN pri članku: 2313-433XDOI: 10.3390/jimaging7060100COBISS_ID: 67727363sl