Segmentation of cancer cell in histopathologic images of breast cancer and lesion area in skin cancer images using convolutional neural networks

Background: Pathological analysis plays an important role in the diagnosis, prediction and planning of cancer treatment. Using digital pathology, ie, scanning and storing digital parts of patient tissue, tools for analyzing these complex images now can be developed. Doctors use a computer diagnostic system from an intelligent assistant to accurately diagnose. These systems have great benefits in improving treatment efficacy.

Methods: In this study, the deep neural network classifier has been used with the help of the Tensor Flow framework and the use of the Keras-library. Input images are initially transmitted from a low pass filter to reduce noise effects. The pre-processed images are then imported into a convolutional neural network.

Results: The results of the research reveal a significant difference in the accuracy values between different methods with the proposed method, which in some cases indicates an increase of more than 14.18% in the accuracy of the diagnosis. Another advantage of the proposed method is to provide high sensitivity to histopathologic images, which shows an increase of 12 to 18 percent compared to other studies. The reason for this is the excellence of extracting high-level features through convolutional neural network, which is accompanied by a reduction in the size of the feature vector.

Conclusion: The results showed a accuracy of %98.6 for skin lesions and %96.1 accuracy for breast cancer histopathologic findings, which offers promising results compared to the results of other studies.