Cascade of classifier ensembles for reliable medical image classification
2014
Medical image analysis and recognition is one of the most important tools in modern
medicine. Different types of imaging technologies such as X-ray, ultrasonography,
biopsy, computed tomography and optical coherence tomography have been widely used
in clinical diagnosis for various kinds of diseases. However, in clinical applications, it
is usually time consuming to examine an image manually. Moreover, there is always
a subjective element related to the pathological examination of an image. This produces
the potential risk of a doctor to make a wrong decision. Therefore, an automated
technique will provide valuable assistance for physicians. By utilizing techniques from
machine learning and image analysis, this thesis aims to construct reliable diagnostic
models for medical image data so as to reduce the problems faced by medical experts
in image examination. Through supervised learning of the image data, the diagnostic
model can be constructed automatically.
The process of image examination by human experts is very difficult to simulate,
as the knowledge of medical experts is often fuzzy and not easy to be quantified.
Therefore, the problem of automatic diagnosis based on images is usually converted to
the problem of image classification. For the image classification tasks, using a single
classifier is often hard to capture all aspects of image data distributions. Therefore,
in this thesis, a classifier ensemble based on random subspace method is proposed to
classify microscopic images. The multi-layer perceptrons are used as the base classifiers
in the ensemble. Three types of feature extraction methods are selected for microscopic
image description. The proposed method was evaluated on two microscopic image sets
and showed promising results compared with the state-of-art results.
In order to address the classification reliability in biomedical image classification
problems, a novel cascade classification system is designed. Two random subspace
based classifier ensembles are serially connected in the proposed system. In the first
stage of the cascade system, an ensemble of support vector machines are used as the
base classifiers. The second stage consists of a neural network classifier ensemble. Using
the reject option, the images whose classification results cannot achieve the predefined
rejection threshold at the current stage will be passed to the next stage for further
consideration. The proposed cascade system was evaluated on a breast cancer biopsy
image set and two UCI machine learning datasets, the experimental results showed that the proposed method can achieve high classification reliability and accuracy with small
rejection rate.
Many computer aided diagnosis systems face the problem of imbalance data. The
datasets used for diagnosis are often imbalanced as the number of normal cases is
usually larger than the number of the disease cases. Classifiers that generalize over the
data are not the most appropriate choice in such an imbalanced situation. To tackle
this problem, a novel one-class classifier ensemble is proposed. The Kernel Principle
Components are selected as the base classifiers in the ensemble; the base classifiers are
trained by different types of image features respectively and then combined using a
product combining rule. The proposed one-class classifier ensemble is also embedded
into the cascade scheme to improve classification reliability and accuracy. The proposed
method was evaluated on two medical image sets. Favorable results were obtained
comparing with the state-of-art results.
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