Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aidradiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodologyto detect masses from mammograms. The K-means clustering algorithm is used to split the mammogramsin regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-massregion. SVM is a machine-learning method, based on the principle of structural risk minimization, whichperforms well when applied to data outside the training set. We use a set of textural and shape measures todetect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements.

High-risk human papillomavirus (HPV) is etiologically related to cervical cancer, other anogenital cancers and oropharyngeal carcinomas. Low-risk HPV, especially HPV6 and HPV11, cause genital warts and laryngeal papillomas. However, the accumulating data suggests that HPV6 and HPV11 may cause malignant lesions at non-cervical anatomic sites. This review aims to estimate the proportions of single and dual HPV6/11 infections in multiple cancers reported in the last 10 years in the Cochrane, Embasa and PubMed databases. Secondly, the genomes of HPV6/11 were compared with the most common high-risk genotype, HPV16, to determine the similarities and differences. A total of 11 articles were selected, including between one and 334 HPV+ cancer patients. The frequencies of single or dual HPV6/11 infections ranged between 0–5.5% for penile and 0–87.5% for laryngeal cancers and were null for vulvar, vaginal and oral cancers. The genomic similarities between HPV6/11 and HPV16 mainly involved the E7 gene, indicating a limited ability to block cell differentiation. The presence of single or dual HPV6/11 infections in variable proportions of penile and laryngeal cancers support the vaccination strategies that cover these genotypes, not only for preventing genital warts but also for cancer prevention. Other risk factors and co-carcinogens are likely to participate in epithelial carcinogenesis associated with low-risk HPV.

Age-related macular degeneration (AMD) is an eye disease that can cause visual impairment and affects the elderly over 50 years of age. AMD is characterized by the presence of drusen, which causes changes in the physiological structure of the retinal pigment epithelium (RPE) and the boundaries of the Bruch’s membrane layer (BM). Optical coherence tomography is one of the main exams for the detection and monitoring of AMD, which seeks changes through the evaluation of successive sectional cuts in the search for morphological changes caused by drusen. The use of CAD (Computer-Aided Detection) systems has contributed to increasing the chances of correct detection, assisting specialists in diagnosing and monitoring disease. Thus, the objective of this work is to present a method for the segmentation of the inner limiting membrane (ILM), retinal pigment epithelium, and Bruch’s membrane in OCT images of healthy and Intermediate AMD patients. The method uses two deep neural networks, U-Net and DexiNed to perform the segmentation. The results were promising, reaching an average absolute error of 0.49 pixel for ILM, 0.57 for RPE, and 0.66 for BM.