Abstract:
Pregnancy is a period when women require extra care. It is necessary to make sure optimal
nutritional level and a well-balanced diet. However, it is regrettable that a huge percentage
of the Bangladeshi population is unaware of this issue. New mothers, in particular, who
have no prior experience with this situation, suffer the most. During this time, an imbalance
of nutrition and inappropriate fruit consumption might lead to major pregnancy
complications. We used a deep learning approach called Convolutional neural network
(CNN) to classify and recognize different types of fruit to solve this problem. Our eight
fruit classes have been divided into two primary categories based on whether they are
beneficial or avoidable. We gathered information about fruit consumption during
pregnancy from gynecologists and various health articles and journals on the internet.
People will learn what fruits they should eat and what fruits they should avoid from our
eight fruit classes by reading our paper. We deployed two pre-trained CNN models,
VGG19 and MobileNetV2, to complete this task. We received our result after running these
two models on our collect dataset. We achieved a training accuracy of 94.06 % and a testing
accuracy of 93.43 % for VGG19. In MobileNetV2, on the other hand, we achieved
accuracy of 90.12 % for training and 89.05 % for testing. VGG19 requires less time than
MobileNetV2 during the training phase.
