Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition

• A novel medical decision support framework for cytological image analysis.
• An efficient hybrid one-class ensemble.
• A novel one-class ensemble pruning method based on a multi-objective memetic algorithm.
• Firefly algorithm for nuclei detection.
• Decision rules for fusing the outputs for nine images into a single prediction for the patient.

Recently a great increase of interest in digital pathology and cytology can be observed. Computer-aided diagnosis solutions, developed to assist physicians in the early detection of diseases, can improve accuracy and robustness of the diagnosis. In this paper we present a work in progress on a computer-aided breast cancer diagnosis. We propose an efficient medical decision support framework that allows distinguishing between benign, malignant and fibroadenoma cases. The nuclei detection procedure is based on the firefly algorithm. The procedure generates nuclei markers that are used in marker-controlled watershed segmentation. Image recognition is done by a novel classifier. Instead of using a multi-class approach we decided to implement one-class decomposition strategy, where each of the classes is represented by an ensemble of one-class classifiers. We propose to use a multi-objective memetic algorithm to select the pool of one-class predictors that display at the same time high diversity and consistency. Experiments conducted on a set of 675 real case medical images obtained from patients of the Regional Hospital in Zielona Góra showed that our framework returns highly satisfactory results, outperforming other state-of-the-art methods.

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