Cancer diseases caused by Human Papilloma Virus (HPV) are the most common killer infectious diseases in the world. Human papilloma virus causes cervical cancer the second most common cancer in women, oral cancer that can cause cancer of the mouth and tongue and anal cancer. Therefore, there is a need to design an automatic HPV-related disease detection model that can assist medical professionals in an early detection of the diseases with a competitive accuracy. rnA convolutional neural network (CNN) is one of deep learning that has been used in computer vision is chosen, for the detection of diseases. CNNs represent an interesting approach for the processing of adaptive images. The algorithm is used for preprocessing, extraction, detection and for evaluating the model's accuracy. rnThe proposed model that is called human papilloma virus caused cancer detection model has a total of eight layers, five convolutions, and three dense layers. It receives 127 Ã— 127 color images and produces two outputs. The proposed model is trained using a total of 66,336 images which includes both infected and healthy images. rnThe validity of our proposed model has been validated through experiments using CNN algorithms acquired from two publicly available pre-trained models namely VGG16 and Inception V3. The detection result shows the proposed model is effective in detecting HPV caused cancers. Based on the detection result the HPV caused cancer detection model has 99.3% detection accuracy and 99.4% testing accuracy. Our contribution of this work is we design a CNN model that can be used for detection of cancers caused by HPV and we have compared three CNN models and found that our CNN model performs better on those models for HPV caused cancer diseases detection.