Today, prostate cancer is one of the most common types of cancer in humans, along with lung cancer and breast cancer. To diagnose it, a physical examination and a PSA analysis are carried out. If there are indications that the patient may have cancer, a biopsy is performed to obtain prostate tissue samples. Afterwards, an expert doctor in pathological anatomy examines these samples and assigns them a score according to the Gleason classification system, which establishes that grades 1 and 2 correspond to a benign prostate tissue, while grades 3, 4 and 5 correspond to a malignant one.

Nowadays, the analysis to classify the samples is a very tedious and time-consuming manual task. In addition, it usually involves a considerable level of subjectivity between different specialists. For this reason, SICAP is born as a project whose main aim is to perform a diagnostic aid system that allows the automatic classification of biopsied samples, according to the Gleason scale. In this way, it would be possible to help the pathologists to improve in terms of time and effectiveness as well as to reduce the level of discordance that exists between them when they try to classify a certain sample.

Biomedical and telecommunications engineers collaborate in the CVBLab group working on the implementation of computational techniques based on Machine Learning and Deep Learning applied to biomedical images, in order to find characteristics and patterns that allow to determine automatically not only if the patient has cancer, but also the severity of it.