Evolutionary Algorithm-Based Classifier Parameter Tuning for Automatic Ovarian Cancer Tissue Characterization and Classification

Abstract

Purpose: Ovarian cancer is one of the most common gynecological cancers in women. It is difficult to accurately and objectively diagnose benign and malignant ovarian tumors using ultrasound and other tests. Hence, there is an imperative need to develop a computer-aided diagnostic (CAD) system for ovarian tumor classification in order to reduce patient anxiety and the cost of unnecessary biopsies. In this paper, we present an automatic CAD system for the detection of benign and malignant ovarian tumors using advanced image processing and data mining techniques.

Materials and Methods: In the proposed system, Hu’s invariant moments, Gabor transform parameters and entropies are first extracted from the acquired ultrasound images. Significant features are then used to train a probabilistic neural network (PNN) classifier for classifying the images into benign and malignant categories. The model parameter (σ) for which the PNN classifier performs the best is identified using a genetic algorithm (GA).

Results: The proposed system was validated using 1300 benign images and 1300 malignant images, obtained from 10 patients with a benign disease and 10 with a malignant disease. We used 23 statistically significant (p < 0.0001) features. By evaluating the classifier using a ten-fold cross-validation technique, we were able to achieve an average classification accuracy of 99.8 %, sensitivity of 99.2 % and specificity of 99.6 % with a σ of 0.264.

Conclusion: The proposed system is automated and hence is more objective, can be easily deployed in any computer, is fast and accurate and can act as an adjunct tool in helping physicians make a confident call about the nature of the ovarian tumor under evaluation.

Zusammenfassung

Ziel: Eierstockkrebs ist eines der häufigsten gynäkologischen Karzinome bei Frauen. Es ist schwierig, durch Sonografie und andere Teste gutartige und bösartige Tumore der Eierstöcke exakt und objektiv zu diagnostizieren. Deshalb ist es zwingend erforderlich, ein computergestütztes diagnostisches (CAD) System für die Klassifikation von Eierstockkarzinomen zu entwickeln, um die Ängste der Patientinnen zu verringern und die von unnötigen Biopsien verursachten Kosten einzusparen. In dieser Publikation stellen wir ein automatisches CAD-System für den Nachweis gutartiger und maligner Eierstockkarzinome vor. Das eine erweiterte Bildverarbeitung und Techniken zur Auswertung größerer Datenmengen (Datamining) verwendet.

Material und Methoden: Im vorgestellten System werden zuerst die Hu‘schen invarianten Momente, die Gabortransformations-Parameter und Entropien aus den erhaltenen Ultraschallbildern extrahiert. Signifikante Merkmale werden dann verwendet, um einen neuralen Wahrscheinlichkeitsnetzwerks(PNN)-Klassifikator zu trainieren, der die Darstellungen in benigne und maligne Kategorien einteilt. Der Modellparameter (σ), für den der PNN-Klassifikator die besten Ergebnisse erzielt, wird unter Verwendung eines genetischen Algorithmus (GA) identifiziert.

Ergebnisse: Das vorgestellte System wurde mittels 1300 benigner Bilder von 10 Patienten mit gutartigen Erkrankungen und 1300 malignen Bildern von 10 Patienten mit Karzinomen überprüft. Wir verwendeten 23 statistisch signifikante (p < 0,0001) Marker. Bei Bewertung des Klassifikators mittels 10-facher Kreuzvalidierung konnten wir eine mittlere Klassifikationsgenauigkeit von 99,8 %, eine Sensitivität von 99,2 % und eine Spezifität von 99,6 % mit einem σ von 0,264 erzielen.

Schlussfolgerung: Das vorgestellte System ist aufgrund der Automatisierung objektiver, es kann in jedem Computer einfach angewandt werden. Es ist schnell und genau und kann den Arzt als ergänzende Methode bei der sicheren Charakterisierung des untersuchten Eierstocktumors unterstützen.

Key words

cervix - 3 D ultrasound - neural networks

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