Cervical cancer causes the abnormal growth of the cells at the cervix region of the woman’s uterus. It is the second most common type of cancer after the breast cancer seen in women. Detection of cervical cancer at the early stage is vital. Various screening methods like Pap smear, Colposcopy and HPV testing are carried out for the detection of cervical cancer. The possible screening techniques to diagnose cervical cancer include the visual inspection of the cervix (VIA), Pap smear examination (cytology), Colposcopy, Biopsy and HPV-DNA detection. All these techniques need the involvement of an expert doctor and/or pathologist. Also, a cancer diagnosis is a subjective process where the experience and training of pathologists are significant factors. Intelligent and automated screening systems will be helpful in such scenarios. This paper presents the methodology for cervical cancer prediction based on pap smear images. Pre-trained deep neural network models are used for feature extraction and different machine-learning (ML) models are trained on extracted features. In the proposed methodology, four pre-trained models such as Alexnet, Resnet-18, Resnet-50 and Googlenet are fine-tuned for feature extraction followed by the different ML algorithms. Among these simple logistic regression, the algorithms have performed best with the highest accuracy of 95.14% with the Alexnet pre-trained model.

Increased use of social Networking sites has resulted into vast image data being uploaded on internet. Organizing this data appropriately for easy retrieval is a tedious task. If the data is organized based on the predefined categories, it will help to speed up the retrieval of data. In this paper an image classification technique has been proposed using Fourier transform to classify the images into predefined classes. To classify the images row mean of Fourier transformed image content are used as features using the wand dataset, the experimentation is carried on different sizes of feature vectors which are formed by taking fractional coefficients of Fourier transformed contents of image. Classification algorithm from different families such as Bayes (Naive Bayes and Bayes Net), Function (RBFNetwork and Simple Logistic), Lazy (IB1 and Kstar), Rule (Decision and Part) and Tree (BFTree, J48 Random Tree and Random Forest) are used for classification. The classification accuracy is used to compare performances of used classifiers with Fourier transform. Simple Logistic classifier with fractional coefficients of Fourier transformed images have given superior classification accuracy among the experimented variants of proposed classification technique.