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Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets

Brodzicki Andrzej, Piekarski Michał, Kucharski Dariusz, Jaworek-Korjakowska Joanna, Gorgon Marek

Foundations of Computing and Decision Sciences

2020

Vol. 45, No. 3

179--193

Deep learning methods, used in machine vision challenges, often face the problem of the amount and quality of data. To address this issue, we investigate the transfer learning method. In this study, we briefly describe the idea and introduce two main strategies of transfer learning. We also present the widely-used neural network models, that in recent years performed best in ImageNet classification challenges. Furthermore, we shortly describe three different experiments from computer vision field, that confirm the developed algorithms ability to classify images with overall accuracy 87.2-95%. Achieved numbers are state-of-the-art results in melanoma thick- ness prediction, anomaly detection and Clostridium difficile cytotoxicity classification problems

Combining Spectral Analysis with Artificial Intelligence in Heart Sound Study

Kucharski Dariusz, Kajor Marcin, Grochala Dominik, Iwaniec Marek, Iwaniec Joanna

Advances in Science and Technology. Research Journal

2019

Vol. 13, no 2

112--118

The auscultation technique has been widely used in medicine as a screening examination for ages. Nowadays, advanced electronics and effective computational methods aim to support the healthcare sector by providing dedicated solutions which help physicians and support diagnostic process. In this paper, we propose a machine learning approach for the analysis of heart sounds. We used the spectral analysis of acoustic signal to calculate feature vectors and tested a set of machine learning approaches to provide the most effective detection of cardiac disorders. Finally, we achieved 91% of sensitivity and 99% of positive predictivity for a designed algorithm based on convolutional neural network.