Acute leukemia is a cancer-related to a bone marrow abnormality. It is more common in children and young adults. This type of leukemia generates unusual cell growth in a short period, requiring a quick start of treatment. Acute Lymphoid Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the main responsible for deaths caused by this cancer. The classification of these two leukemia types on blood slide images is a vital process of and automatic system that can assist doctors in the selection of appropriate treatment. This work presents a convolutional neural networks (CNNs) architecture capable of differentiating blood slides with ALL, AML and Healthy Blood Slides (HBS). The experiments were performed using 16 datasets with 2,415 images, and the accuracy of 97.18% and a precision of 97.23% were achieved. The proposed model results were compared with the results obtained by the state of the art methods, including also based on CNNs.Index Terms-leukemia diagnosis, convolutional neural network, computer aided diagnosis.

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The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

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First, I would like to thank my family: my grandmother Rosalha Borges, my mother Luciane Borges, my aunt Crisitiane Borges, and my brother Diogo Borges, for always being there for me and supporting me in life, especially in difficult moments such as this one where I had to absent from their presence to accomplish this work. Without them I would not be able to fulfill this work. I would like to thank my adviser, Prof. Markus Endler, to whom I have an immense gratitude and admiration for his excellent guidance, availability, patience and for having given me the honor of working with him. His research vision has inspired and shaped me into becoming a better researcher. I'm sure our research partnership will continue after the end of my PhD. I would also like to thank Prof. Francisco Silva. His critical view guided and shaped numerous parts of this thesis. I learned a lot from him. In the same way, I would also like to thank Prof. Marco Casanova and Prof. Hélio Lopes for encouraging me to work with this topic and providing numerous suggestions, especially the insight for the density heuristic. I would like to thank my roommates during the PhD journey: Àlan Guedes, Eduardo Araújo, Derlyane Simão, Katia Vega, and Thais Abreu. I learned a lot living with them and they made the PhD journey with more joy and happiness. I will miss you all. In addition to my funny roommates, I would also like to thank all my friends (1801!