Additionally, dyes can sometimes result in false identification due to their invasive properties. Ĭancer cells can be evaluated with the help of fluorescent markers, but phototoxicity and photobleaching are the main concerns. Separated CTCs in microfluidic devices can be imaged using microscopy. Microfluidic devices contain channels in one dimension in the order of micrometers that can help to maintain the flow of liquid and separate CTCs from blood cells due to their larger size. LB can be performed in following ways: 1) using markers for CTC detection and 2) label-free CTC detection using microfluidic devices. LB focuses on isolating circulating cancer cells (CTCs) from the peripheral blood of cancer patients for detecting and monitoring cancer progression. Tissue biopsy is invasive as it requires surgical tools to extract cancer cells from the tumor. Biopsies can be divided into two types: tissue and liquid biopsy (LB). A final diagnosis of cancer is based on an examination of tissue or cells under a microscope by a pathologist. The sensitivity and accuracy of markers vary and increase with their concentration, but they do not always indicate the presence of cancer. The detection of cancer markers or their concentration may indicate the emergence of cancer or its recurrence. Cancer tissues secrete cancer markers into the blood. The number of cancer markers varies according to the cancer activity in the bloodstream. To diagnose cancer through laboratory tests, cancer markers are used to monitor cancer in the blood. Situations, where MRI is used, include examining cancer or sarcoma in the head and neck region. MRI is a procedure that uses powerful magnetic fields and does not generate ionizing radiation. The most common imaging method used to detect cancer and monitor its spread is CT, which provides cross-sectional imaging using a computer and X-rays. Imaging methods include computed tomography (CT) and magnetic resonance imaging (MRI). Conventional methods for diagnosing cancer include imaging techniques, blood sample extraction, and biopsy of tissue for cell examinations. The accuracy of the two strategies is analyzed and the deep learning strategy outperforms feature-based classification by about 9% with the 10-fold cross-validation evaluation.Ĭancer is one of the most common and fatal diseases in the world, and diagnosing it is a very critical and challenging task. For image-based classification, two types of deep learning CNN models are trained: skip connections (Resnet) and without the skip connection. In feature-based classification, several features related to both the intracellular material and thickness of cancer cells are extracted, followed by the feature selection and the training of random forest, support vector machine, and pattern recognition artificial neural networks. Digital holography in a microscopic configuration is used to obtain stain-free quantitative phase images representing the intracellular content and morphology of cells. Herein, the classification of three types of cancer cell lines (lung, breast, and skin) by feature-based machine learning and image-based deep learning with a convolutional neural network (CNN) is addressed. Image-based stain-free elliptical cancer cell classification is very challenging due to interclass morphological similarity.
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