## 1 Introduction  
Helminthiasis remains one of the serious public health concerns that affects millions of people in the world including Malaysia. The global report indicates that more than 1.5 billion people are diagnosed with helminthiasis through soil-transmitted helminth infection, representing 24% of the worldwide population [1]. The _Ascaris Lumbricoides_ species affects the most people with an estimated 807-1121 million, followed by the _Trichuris Trichiura_ species with approximately 604-795 million and _Hookworm_ species with around 576-740 million [2]. Children between the ages of 5 and 15 are particularly vulnerable to infection, which can have a negative impact on their physical, mental, and emotional well-being [3]. In 2020, more than 436 million children in the world were treated for helminthiasis disease [1]. Helminth infection is transmitted to human beings through the soil, water, food, and molluscan vectors. After the contamination of soil by human or animal feces, the helminth ovom or larvae begins to develop and survive in soil with a favorable environment. Uponskin contact with tainted soil or ingestion of polluted soil, water, or food, people will become infected. Patients with light helminth infections usually do not suffer from symptoms whereas patients with heavier infections will suffer from symptoms including diarrhea, abdominal pain, chronic intestinal blood loss, anemia, loss of appetite, weakness, and malnutrition. Helminth worms absorb nutrients from the host tissue, which includes blood, this causes massive iron and protein loss in the human body [4]. Helminth not only infects the intestine but also other organs including the liver, brain, lungs, and blood [5]. Helminth infection can be life threatening if not diagnosed and treated immediately, especially in children and pregnant women. Usually, manual observation under a microscope is one of the methods used by parasitologists to detect the different types of helminth ova. Based on the size and structure of the helminth ovu, identification for the types of helminth can be conducted. However, the microscopic samples are generally embedded with unnecessary components that share common traits including color and shape with the helminth ova. Hence the ova are easily mistakenly identified. Thus, it will reduce the accuracy of diagnosis. Moreover, this manual observation method is inefficient when there are many samples. This is due to humans having limitations on accurately observing the samples in a specific period. In addition, this examination method is very time consuming and costly due to the complicated procedures. These approaches are not suitable for field surveys or the rapid identification of those most in need of treatment [6]. Many image processing and classification techniques have been approached to recognize the helminth species either by using machine learning or deep learning to overcome these manual observation limitations. These image processing and classification techniques using machine learning and deep learning will be set up in the computerized microscopic lab in order to help the deliverability to medically underserved communities. Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. It is used to teach machines how to handle the data more efficiently [7]. A machine learning system was proposed by [PERSON] [8], which identifies and quantifies seven species of helminth in wastewater at different development stages. In the preprocessing stage, grey-scale conversion, anisotropic diffusion filter, and Gaussian derivative function with image binarization were applied to enhance the outer shell of the ova and segment the image. A naive Bayesian classifier was used and obtained a specificity of 99% and a sensitivity of 84%. In deep learning, there are several neural networks such as convolutional neural network (CNN) and recurrent neural network (RNN).