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Role of image processing in the cancer diagnosis

100%

Detyna J. , Jeleń Ł. , Jeleń M.

2011

tom Vol. 7, no. 4

5--9

Cancer is still one of the most deadly diseases. It is a well known fact that the early diagnosis is crucial and allows for the successful treatment while cancers diagnosed in their late stage are almost impossible to treat. For precise and objective diagnosis there is a need for a computerized method for cytological image processing, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy. In particular, issues of image processing in the aspect of medical diagnosis presented by prof. R. Tadeusiewicz and Dr. J. Śmietański in [1].

Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique

63%

Mishra P. , Bhoi N.

2021

tom Vol. 41, no. 3

916--932

Recognizing the cancer genes from the microarray dataset is considered as the most essential research topic in bioinformatics and computational biology domain. Microarray dataset represents the state of each cell at the molecular level which is identified as the important diagnostic tool in medical field. Analyzing the microarray data may provide a huge support for cancer gene classification. Therefore recently a number of artificial intelligence and machine learning techniques are developed which utilize the microarray data for distinguishing the cancer and non-cancer cells. But still now these techniques does not achieved a satisfactory performance. Therefore, an efficient technique that provides a crisp output for cancer classification is required. To overcome such defect, an enhanced ANFIS (EANFIS) method is used in this proposed architecture for classifying the cancer genes. The convergence time of ANFIS gets increased during learning process, therefore to avoid such issue the Manta ray foraging optimization (MaFO) algorithm is hybrid along with ANFIS which improves the overall classification performance. The data given as an input to the classification process is pre-processed at the initial phase using the Ensemble Kalman Filter (EnKF) technique. After pre-processing, the genes having similar properties are clustered using an adaptive density-based spatial clustering with noise (ADBSCAN) clustering technique. Finally, the performance of proposed enhanced ANFIS is evaluated using the precision, accuracy, f-measure, recall, sensitivity, and specificity metrics. Further, the clustering based performance evaluation is also carried out using the cluster index metrics. Finally, the comparison with the state-of-the-art techniques is also performed to show the effectiveness of proposed approach.