Abstract
Fourier transform infrared spectroscopy (FT-IR) is an effective diagnostic tool for the characterization of biological molecules that may provide uninvasive, rapid detection of gastric cancer in vivo in the future. FT-IR spectra were obtained from 103 endoscopic biopsy gastric tissue samples that included 20 healthy, 35 chronic atrophic gastritis, 29 chronic superficial gastritis, and 19 gastric cancerous samples. A new approach using entropy weight local-hyperplane k-nearest neighbor based on frequency domain information (EWHFI) is proposed that improves and extends the adaptive weight local-hyperplane k-nearest neighbor (AWHK) approach. EWHFI combines the AWHK algorithm with the fine Fourier transform information in the frequency domain to effectively distinguish similar pathological states. The experimental results show that EWHFI enhances the accuracy, sensitivity, and specificity for the diagnosis of early gastric cancer. The average results of the random classification showed that the EWHFI classifiers for differentiating gastric cancerous, chronic atrophic gastritis, chronic superficial gastritis, and healthy tissues were 95.1%, 86.4%, 88.3%, and 99%, respectively.
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