An automated cervical pre-cancerous diagnostic system Nor Ashidi Mat-Isa a, * , Mohd Yusoff Mashor b,1 , Nor Hayati Othman c,2 a Center for Electronic Intelligent System (CELIS), School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia b School of Mechatronic Engineering, Universiti Malaysia Perlis, Kubang Gajah Campus, 02600 Arau, Perlis, Malaysia c School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia Received 21 February 2007; received in revised form 20 September 2007; accepted 24 September 2007 Artificial Intelligence in Medicine (2008) 42, 1—11 http://www.intl.elsevierhealth.com/journals/aiim KEYWORDS Diagnostic system; Neural network; Pattern analysis; Region growing; Feature extraction; Cervical cancer Summary Objective: This paper proposes to develop an automated diagnostic system for cervical pre-cancerous. Methods and data samples: The proposed automated diagnostic system consists of two parts; an automatic feature extraction and an intelligent diagnostic. In the automatic feature extraction, the system automatically extracts four cervical cells features (i.e. nucleus size, nucleus grey level, cytoplasm size and cytoplasm grey level). A new features extraction algorithm called region-growing-based features extraction (RGBFE) is proposed to extract the cervical cells features. The extracted features will then be fed as input data to the intelligent diagnostic part. A new artificial neural network (ANN) architecture called hierarchical hybrid multilayered perceptron (H 2 MLP) network is proposed to predict the cervical pre-cancerous stage into three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL) and high grade intra-epithelial squamous lesion (HSIL). We empirically assess the capability of the proposed diagnostic system using 550 reported cases (211 normal cases, 143 LSIL cases and 196 HSIL cases). Results: For evaluation of the automatic feature extraction performance, correlation test approach was used to determine the capability of the RGBFE algorithm as compared to manual extraction by cytotechnologist. The manual extraction of size was recorded in micrometer while the automatic extraction of size was recorded in number of pixels. Region color was recorded in mean of grey level value for both manual and automatic extraction. The results show that the estimated size and mean * Corresponding author. Tel.: +60 4 5996051; fax: +60 4 5941023. E-mail addresses: ashidi@eng.usm.my (N.A. Mat-Isa), yusoff@unimap.edu.my (M.Y. Mashor), hayati@kb.usm.my (N.H. Othman). 1 Tel.: +60 4 9798335; fax: +60 4 9798334. 2 Tel.: +60 9 7663117, 60 9 765 8371; fax: +60 9 7656291. 0933-3657/$ — see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.artmed.2007.09.002