An efficient CNN based algorithm for detecting melanoma cancer regions in H&E-stained images.

Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.

Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin (H&E) stained histopathological images. In this technique, the nuclei in the image are first segmented using a Convolutional Neural Network (CNN). The segmented nuclei are then used to generate melanoma region masks. Experimental results with a small melanoma dataset show that the proposed method can potentially segment the nuclei with more than 94 % accuracy and segment the melanoma regions with a Dice coefficient of around 85 %. The proposed technique also has a small execution time making it suitable for clinical diagnosis with a fast turnaround time.

Automated proliferation index calculation for skin melanoma biopsy images using machine learning.

The Proliferation Index (PI) is an important diagnostic, predictive and prognostic parameter used for evaluating different types of cancer. This paper presents an automated technique to measure the PI values for skin melanoma images using machine learning algorithms. The proposed technique first analyzes a Mart-1 stained histology image and generates a region of interest (ROI) mask for the tumor. The ROI mask is then used to locate the tumor regions in the corresponding Ki-67 stained image. The nuclei in the Ki-67 ROI are then segmented and classified using a Convolutional Neural Network (CNN), and the PI value is calculated based on the number of the active and the passive nuclei. Experimental results show that the proposed technique can robustly segment (with 94 % accuracy) and classify the nuclei with a low computational complexity and the calculated PI values have less than 4 % average error.

Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images.

The lymphatic system is the immune system of the human body, and includes networks of vessels spread over the body, lymph nodes, and lymph fluid. The lymph nodes are considered as purification units that collect the lymph fluid from the lymph vessels. Since the lymph nodes collect the cancer cells that escape from a malignant tumor and try to spread to the rest of the body, the lymph node analysis is important for staging many types skin and breast cancers. In this paper, we propose a Computer Aided Diagnosis (CAD) method that segments the lymph nodes and melanoma regions in a biopsy image and measure the proliferation index. The proposed method contains two stages. First, an automated technique is used to segment the lymph nodes in a biopsy image based on histogram and high frequency features. In the second stage, the proliferation index for the melanoma regions is calculated by comparing the number of active and passive nuclei. Experimental results on 76 different lymph node images show that the proposed segmentation technique can robustly segment the lymph nodes with more than 90% accuracy. The proposed proliferation index calculation has low complexity and has an average error rate of less than 1.5%.