Classification and enhancement of invasive ductal carcinoma samples using convolutional neural networks

In the medical area, the detection of invasive ductal carcinoma is the most common sub-type of all breast cancers; about 80% of all breast cancers are invasive ductal carcinomas. Detection of this type of cancer shows a great challenge for specialist doctors since the digital images of the sample must be analyzed by sections because the spatial dimensions of this kind of image are above 50k × 50k pixels; doing this operation manually takes long time to determine if the patient suffers this type of cancer. Time is essential for the patient because this cancer can invade quickly other parts of the body. Its name reaffirms this characteristic, with the term "invasive" forming part of its name. With the purpose of solving this task, we propose an automatic methodology consisting in improving the performance of a convolutional neural network that classifies images containing invasive ductal carcinoma cells by highlighting cancer cells using several preprocessing methods such as histogram stretching and contrast enhancement. In this way, characteristics of the sub-images are extracted from the panoramic sample and it is possible to learn to classify them in a better way.

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Diabetic retinopathy (DR) is a common eye disease, which leads cause of blindness all around the world. Microaneurysms (MAs) is one of the early symptoms of DR. Accurate and effective MAs detection and segmentation is an important step for the diagnosis and treatment of DR. In this paper, we propose an automatic model for detection of MAs in fluorescein fundus angiography (FFA) images. The model mainly consists of two steps. The first step is pre-processing of FFA images, where the quality of FFA images is improved by Histogram Stretching and Gaussian Filtering algorithm. The second step is to detect MAs regions, where the MAs regions are detected by improved FC-DenseNet. We compare the proposed model with traditional FC-DenseNet model and other previously published models. The experimental result shows that our proposed model has the highest scores on evaluation metrics of pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1) and mean intersection over union (MIoU), which are 99.97%, 94.19%, 88.40%, 89.70%, 88.98% and 90.14%, respectively. The result suggests that the performance of our proposed model is closer to the ground truth of MAs detection. Our proposed model would be helpful for ophthalmologists to find the symptoms more quickly and to take better treatment measures in the screening process of diabetic retinopathy.