Istopathology Image Colorization for Diagnosis

This paper reviews Histopathology image colorization for diagnosis. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analysing and classifying most of the diseases like cancer. The analysis of histopathological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The histopathologist meticulously evaluates tissue architecture, cellular arrangement, and anomalies in cell morphology to discern between benign and malignant conditions within the image. However, this process is labor-intensive and susceptible to both intra and inter-observer variability. To mitigate these challenges, there arises a necessity for computer-assisted image analysis, facilitating a quantitative approach to tissue diagnosis. The process which is useful to analyse the tissue. Pathologist who can observed the tissue under the microscope which has low resolution by colorization of the tissue which gives the clarity in the tissue. The actual process which can be done by the manually which would be taken the long time and accuracy rate would the low. When use this model it would give the highly accuracy rate and also take the less time to analyse the tissue. In this model we have trained the thousands of tissues which contain the non-cancer tissues and the cancer tissues, When trained with the large dataset then it would give the accuracy rate also high