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This paper presents a novel algorithm for computer-assisted classification of cervical cancers using digitized histology images of biopsies. Texture analysis of the nuclei structure is very important for classification of cervical cancer histology. In this paper we present a two-tier classification strategy using Gabor filter banks for local classification and abnormality spread for global taxonomy. The test data used in this work are digitized histology images of cervical biopsies acquired from the pathology laboratories in the Saiful An war Hospital in Indonesia. The images from over 500 subjects are categorized by the pathologists into five grades, benign, pre-cancer (CIN1, CIN2, CIN3) and malignant. In the algorithm developed in this work, a texture classification method using Gabor filter banks is implemented to segment the image into five possible regions: of background, normal, abnormal, basal and stroma cells. The …