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The prediction of the accurate state and recurrence of cancer is critical. Histological grade, which is commonly used by physicians currently, is determined by pathologists by performing a semi-quantitative analysis of three histological and cytological features on Hematoxylin-Eosin (HE) stained histopathological images, to determine the treatment option for the patient and to assess the prognosis of a cervical cancer patient. Computerized image processing technology has been proved to improve consistency, efficiency and accuracy in histopathology evaluations, and can also provide decision support to ensure diagnostic consistency. This paper examines cervical disease recognition utilizing Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) by utilizing these histopathological pictures.