Breast cancer represents one of the leading causes of death among women, with 1 in 39 (around 2.5%) of them losing their lives annually, at the global level. According to the American Cancer Society, it is the second most lethal type of cancer in females, preceded only by lung cancer. Early diagnosis is crucial in increasing the chances of survival. In recent years, the incidence rate has increased by 0.5% per year, with 1 in 8 women at increased risk of developing a tumor during their life. Despite technological advances, there are still difficulties in identifying, characterizing, and accurately monitoring malignant tumors. The main focus of this article is on the computerized diagnosis of breast cancer. The main objective is to solve this problem using intelligent algorithms, that are built with artificial neural networks and involve 3 important steps: augmentation, segmentation, and classification. The experiment was made using a publicly available dataset that contains medical ultrasound images, collected from approximately 600 female patients (it is considered a benchmark). The results of the experiment are close to the goal set by our team. The final accuracy obtained is 86%.

乳腺癌是女性死亡的主要原因之一，在全球范围内，每年有三十九人中就有一人（约 2.5%）死于乳腺癌。根据美国癌症协会的数据，它是女性第二大致命癌症，仅次于肺癌。早期诊断对于增加生存机会至关重要。近年来，发病率每年增加 0.5%，每 8 名女性中就有 1 人一生中患肿瘤的风险增加。尽管技术进步，但在识别、表征和准确监测恶性肿瘤方面仍然存在困难。本文的主要重点是乳腺癌的计算机化诊断。主要目标是使用智能算法来解决这个问题，这些算法是用人工神经网络构建的，涉及 3 个重要步骤：增强、分割和分类。该实验是使用公开的数据集进行的，其中包含从大约 600 名女性患者收集的医学超声图像（被视为基准）。实验结果接近我们团队设定的目标。最终获得的准确率为86%。