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• Proposed a deep learning model to detect cervical cancer. • Proposed a cell merger model to classify in a similar way to real smear examination. • Classification of squamous cells without prior image preprocessing. • Classification of squamous cells in HSIL, LSIL, ASCUS and normal. • Detection of cervical squamous atypiae with 88.8% accuracy in classification. Early detection of cancer is important to improve survival and reduce associated morbility. Nowadays, there is no automatic classification process with enough accuracy to be recommended to its use in population cervical cancer screening. In most automatic medical image classifications, these images are clean in background and without overlap between elements, which means that these images do not reflect reality and the model cannot be applied to directly obtained images from medical samples. The objectives of this study are to design and implement a Cell Merger Approach to improve the efficiency and realism of the PAP-smears classification model, by allowing overlapping and folding of different cells, to design and implement a Convolutional Neural Network for PAP-smears image classification, and to optimize and integrate the cell fusion approach with the neural network building a feasible, reliable and highly accurate system for cervical smears classification. The carried out experiments have validated both the CNN and the proposed Cell Merger Approach with very interesting results. The most outstanding results show that the Convolutional Neural Network models together with the Cell Merger Approach have a classification accuracy of 88.8% with a standard deviation of 1%, obtaining a sensitivity and specificity of 0.92 and 0.83 respectively. This classification level depicts a robust and accurate model that is comparable to an expert pathologist competencies. [ABSTRACT FROM AUTHOR]