Abstract
As of now, the analysis of sperm such as counting and detection processes are still operated manually which tends to produce errors. False detection in sperm analysis must be minimized as possible. Therefore, the current study focuses on developing a Sprague Dawley rat sperm classification system to assist the detection process by pathologist. The system has the ability to classify the Sprague Dawley rat sperm into two classes namely normal and abnormal based on the morphological characteristic of sperm's head. The proposed system employs the Hybrid Multilayered Perceptron (HMLP) neural network trained with the Modified Recursive Prediction Error (MRPE) algorithm as the intelligent classifier tool. This study will also investigate three significant morphological characteristics of rat sperm's head namely opened degree, width of the curve and template matching percentage as the input data for the HMLP network. Furthermore, this study further classifies the abnormal sperm into hookless and banana shape. Promising result with 100% and 94.62% of accuracy has been achieved for two classes and three classes classification of rat sperm respectively.
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