HEp-2 CELL CLASSIFICATION BY ADAPTIVE CONVOLUTIONAL LAYER BASED CONVOLUTIONAL NEURAL NETWORK

Abstract

Objective: The antinuclear antibodies (ANA) that present in the human serum have a link with various autoimmune diseases. Human Epithelial type-2 (HEp-2) cells acts as a substance in the Indirect Immuno fluorescence (IIF) test for diagnosing these autoimmune diseases. In recent times, the computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has drawn more interest. Though, they often pose limitations like large intra-class and small inter-class variations. Hence, various efforts have been performed to automate the procedure of HEp-2 cell classification. To overcome these problems, this research work intends to propose a new HEp-2 classification process. Materials and Methods: This is regulated by integrating two processes, namely, segmentation and classification. Initially, the segmentation of the HEp-2 cells is carried out by deploying the morphological operations. In this paper, two morphology operations are deployed called opening and closing. Further, the classification process is exploited by proposing a modified Convolutional Neural Network (CNN). The main objective is to classify the HEp-2 cells effectively (Centromere, Golgi, Homogeneous, Nucleolar, NuMem, and Speckled) and is made by exploiting the optimization concept. This is implanted by developing a new algorithm called Distance Sorting Lion Algorithm (DSLA), which selects the optimal convolutional layer in CNN. Results: Through the performance analysis, the performance of the proposed model for test case 1 at learning percentage 60 is 3.84%, 1.79%, 6.22%, 1.69%, and 5.53% better than PSO, FF, GWO, WOA, and LA, respectively. At 80, the performance of the proposed model is 5.77%, 6.46%, 3.95%, 3.24%, and 5.55% better from PSO, FF, GWO, WOA, and LA, respectively. Hence, the performance of the proposed work is proved over other models under different measures. Conclusion: Finally, the performance is evaluated by comparing it with the other conventional algorithms in terms of accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, MCC, F1-Score and FDR, and proves the efficacy of the proposed model.

Optimised hybrid classifiers for automatic HEp‐2 cell classification

“…The HEp‐2 data set was downloaded from http://qixianbiao.github.io/HEp2Cell/ (on 17.04.2019) [32, 33] which were divided into ten test cases like test case 1, test case 2, test case 3, and so on. Here, the performance of the adopted method was distinguished over the other conventional schemes like GWO [31], WOA [34], LA [35], and DSLA [25] in terms of statistical analysis. In addition, the analysis was carried out by examining the performance measures, such as accuracy, specificity, precision, and F 1‐score in terms of varying learning percentages to 20, 40, 60, and 80%.…”

“…In 2019, Manju and Jose [25] developed a new HEp‐2 classification process by employing two morphology operations namely opening and closing. The main intention was to classify the HEp‐2 cells successfully, which was done through the presented distance sorting lion algorithm (DSLA) method.…”

“…Such limitations have to be considered for HEp‐2 cell classification effectively. The DSLA [25] method was employed to classify the HEp‐2 cells. In addition, it selects the optimal convolutional layer on CNN.…”