PREDICTING POLYCYSTIC OVARY SYNDROME THROUGH MACHINE LEARNING TECHNIQUE USING PATIENTS' SYMPTOM DATA AND OVARY ULTRASOUND IMAGES /

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Bibliographische Detailangaben
1. Verfasser:	Suha, Sayma Alam
Körperschaft:	Supervised by Lt Col Muhammad Nazrul Islam (Assoc. Professor, Dept. of CSE, MIST.)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Dhaka : CSE DEPT. MIST, c2022.
Schlagworte:	Computer Science and Engineering. This Thesis Paper of CSE in M.Sc. Program.
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