Since 1978, In Vitro Fertilization has been defined as the couple's inability to conceive for at least one year with timely sexual intercourse without any birth control [2]. The IVF process involves the collection of embryos that must be inseminated by sperm under clinical conditions. These fertilized embryos are under observation for at least a period of 2-5 days. The good embryo for implantation will be selected by embryologists and then transferred to the woman's uterus on day 2 or day 5. Checking the viable embryo is a tedious process involving experts such as embryologists. physically present. But success still remains 20-25%, due to failure to identify a potential embryo. To have a chance of pregnancy, multiple embryos will be transferred into the woman's womb. This multiple transfer will be complicated for both mother and baby. Different researchers have looked for various solutions to clearly identify and transfer a single embryo. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The overall IVF treatment depends on the individual cycle response, patient acceptance ability, clinical aspects, embryo viability, equipment technology. Personal experiences of individuals such as patients, doctors and embryologists. Machine learning techniques can be applied to the IVF process to increase selection efficiency. A model can be designed to evaluate these embryos for the implantation process, which will train itself with certain parameters providing automated decision support to embryologists when the need exists. In contrast to the emergence and importance of decision support systems in the IVF process, related literature is limited. Artificial neural networks (ANN), convolutional neural networks (CNN), ReLU network classifiers and also prediction models are used in neural network to get accurate results in IVF treatment. Machine learning techniques are prediction models in which the network learns to perform digital image classification or any task directly from a given set of images, text or sound. The medical data obtained will be in text format. Recovering such data becomes complex. Machine learning is usually implemented using neural network architecture, here in this article the machine learning model is performed by training the network through datasets obtained from different hospitals. Once the data is trained, analysis on any image can be achieved very easily. Machine learning networks generally contain several connected layers of convolutional neural networks that can be driven by classifiers. Machine learning techniques are giving better results than Hugh transform algorithm and multiscale vessel filtering. Applying these techniques improved performance by 96.7% and training the network is faster than previous algorithms. Recognizing the viability of human embryos from microscopic images is an extremely tedious process, susceptible to errors and subject to intra- and inter-individual unpredictability. Please note: this is just an example. Get a custom paper from our expert writers now. Get Custom Assay Automated classification of these embryo images will have the advantage of reducing time and cost, minimizing errors and improving results, consistency of results between.