2 research outputs found
Background suppressing Gabor energy filtering
In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%
Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
Skin lesions known as naevi exhibit diverse characteristics such as size,
shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into
play when monitoring for melanoma, referring to a lesion with distinctive
features that sets it apart from other lesions in the vicinity. As lesions
within the same individual typically share similarities and follow a
predictable pattern, an ugly duckling naevus stands out as unusual and may
indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD)
has become a significant player in the research and development field, as it
combines machine learning techniques with a variety of patient analysis
methods. Its aim is to increase accuracy and simplify decision-making, all
while responding to the shortage of specialized professionals. These automated
systems are especially important in skin cancer diagnosis where specialist
availability is limited. As a result, their use could lead to life-saving
benefits and cost reductions within healthcare. Given the drastic change in
survival when comparing early stage to late-stage melanoma, early detection is
vital for effective treatment and patient outcomes. Machine learning (ML) and
deep learning (DL) techniques have gained popularity in skin cancer
classification, effectively addressing challenges, and providing results
equivalent to that of specialists. This article extensively covers modern
Machine Learning and Deep Learning algorithms for detecting melanoma and
suspicious naevi. It begins with general information on skin cancer and
different types of naevi, then introduces AI, ML, DL, and CAD. The article then
discusses the successful applications of various ML techniques like
convolutional neural networks (CNN) for melanoma detection compared to
dermatologists' performance. Lastly, it examines ML methods for UD naevus
detection and identifying suspicious naevi