Automatic Quality Assessment of Dermatology Images : A Comparison Between Machine Learning and Hand-Crafted Algorithms

In recent years, pictures from handheld devices such as smartphones have been increasingly utilized as a documentation tool by medical practitioners not trained to take professional photographs. Similarly to the other types of image modalities, the images should be taken in a way to capture the vital information in the region of interest. Nevertheless, image capturing cannot always be done as desired, so images may exhibit different blur types at the region of interest. Having blurry images does not serve medical purposes, therefore, the patients might have to schedule a second appointment several days later to retake the images. A solution to this problem is to create an algorithm which immediately after capturing an image determines if it is medically useful and notifies the user of the result. The algorithm needs to perform the analysis at a reasonable speed, and at best, with a limited number of operations to make the calculations directly in the smartphone device. A large number of medical images must be available to create such an algorithm. Medical images are difficult to acquire, and it is specifically difficult to acquire blurry images since they are usually deleted. The main objective of this thesis is to determine the medical usefulness of images taken with smartphone cameras, using both machine learning and handcrafted algorithms, with a low number of floating point operations and a high performance. Seven different algorithms (one hand-crafted and six machine learned) are created and compared regarding both number of floating point operations and performance. Fast Walsh-Hadamard transforms are the basis of the hand-crafted algorithm. The employed machine learning algorithms are both based on common convolutional neural networks (MobileNetV3 and ResNet50) and on our own designs. The issue with the low number of medical images acquired is solved by training the machine learning models on a synthetic dataset, where the non-medically useful images are generated by applying blur on the medically useful images. These models do, however, undergo evaluation using a real dataset, containing medically useful images as well as non-medically useful images. Our results implicate that a real-time determination of the medical usefulness of images is possible on handheld devices, since our machine learned model DeepLAD-Net reaches the highest accuracy with 42 · 106 floating point operations. In terms of accuracy, MobileNetV3-large is the second best model with31 times as many floating point operations as our best model