Breast Cancer Classification Using Machine Learning. An Empirical Study (eBook)

Akor Ugwu (Autor)

eBook Download: PDF

2021 | 1. Auflage
GRIN Verlag
978-3-346-40482-4 (ISBN)

Infos

Diploma Thesis from the year 2020 in the subject Medicine - Diagnostics, grade: 3.55, , course: Computer Science, language: English, abstract: The study will classify breast cancers into foremost problems: (Benign tumor and Malignant tumor). A benign tumor is a most cancers does now not invade its surrounding tissue or spread around the host. A malignant tumor is another kind of cancers which can invade its surrounding tissue or spread around the frame of the host. Benign cancers on uncommon event can also surely result in someone’s death, but as a fashionable rule they're no longer nearly as horrific because the malignant cancers. The malignant cancers at the contrary are like those killer bees. In this situation, you do not need to be doing something to them or maybe be everywhere near their hive, they will just spread out and attack you emass – they could even kill the individual if they are extreme enough.

Manual manner of cancer category into benign and malignant may be very tedious, susceptible to human error and unnecessarily time consuming. The proposed system while constructed can robotically classify the sort of most cancers into the safe (benign) and also the risky (malignant). This machine plays this role through the usage of machine getting to know algorithm. The following is the extensive of this new system: Classification mistakes could be notably removed, early analysis of disorder, removal of possible human mistakes and the device does no longer die. However, the researcher seeks to detect and assess the class of breast using Machine learning.

Produkt-Details

• Erscheint lt. Verlag: 11.5.2021
• Verlagsort: München
• Sprache: englisch
• Themenwelt: Medizin / Pharmazie ► Medizinische Fachgebiete
• Themenwelt: Studium ► 2. Studienabschnitt (Klinik) ► Anamnese / Körperliche Untersuchung
• Schlagworte: Breast • Cancer • classification • Empirical • learning • machine • Study • Using
• ISBN-10: 3-346-40482-X / 334640482X
• ISBN-13: 978-3-346-40482-4 / 9783346404824