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Introduction: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images. Methods: We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Results: According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy). Conclusions: ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images. [ABSTRACT FROM AUTHOR]

• 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]