MALARIA DETECTION USING MACHINE LEARNING /
TAFADZWANASHE BLESSINGS VAKI; SUPERVISOR: ASST. PROF. DR. EMRE ÖZBİLGE
- ix, 54 sheets; 31 cm. Includes CD
Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department
Includes bibliography (sheets 50-54)
ABSTRACT Malaria is a very serious and sometimes a deadly sickness resulting from a parasite that is commonly found in female anopheles' mosquitos which feed on human beings. Humans that contract malaria are usually very sick with flu-like contamination, very high fevers, and shaking chills. The highest rates of malaria infections are found in African countries. This research was done to provide a cost effective machine learning method to perform malaria diagnosis in Africa especially in the rural areas. The research of this study was carried out using the following methodology. 27,558 images containing equals parts of infected and uninfected blood cells then were further divided into test, training and validation sets. The model was created using convolutional neural networks(CNN) which part of deep learning. Five layers of convolution were used with max pooling layers between the convolution layers. Binary crossentropy was used to calculate the loses. To evaluate the custom CNN model, the metrics measured were accuracy, f1 score, precision and recall. The results of the research showed that the proposed custom CNN model had 96.1% accuracy, an F1 score 0.96, precision score 0.968 and a recall score of 0.95. These results indicated a successful custom CNN implementation. The conclusions and future recommendations are also discussed in this research to further improve accuracy and help the fight against malaria in the rural areas of Africa. Keywords: CNN, Deep learning, Machine learning, Malaria