Authors - S.D.P. Abeysekara, J.A.D.N. Jayakody, K.A. Dilini T. Kulawansa Abstract - Breast cancer is the second most prevalent cancer globally and a leading cause of death among women. According to the World Health Organization, over 2.3 million new cases are diagnosed annu ally, emphasizing the need for early and accurate detection.In this work, Wavelet-Driven Intelligent Model for Multi-Class Breast Cancer Diagno sis is proposed. In this proposed work, three level wavelet decomposition is used on BreakHis data to extract wavelet based features. These fea tures were fed to Artificial Neural Network Classifiers such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Machine Learning Classifier Random Forest (RF). Multi-class classification (binary , be nign sub-types, 4 malignant sub-types) of breast tumour has been done. The experimental results show that RF achieved high accuracy of 94% for benign and malignant, 97% for benign sub- type and 92% for malig nant subtype classification compared to RBF and MLP. Deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are more effective when trained on large-scale datasets but for small datasets and limited resource environments, the proposed framework ensures efficient and consistent diagnostic approach. In future, a prototype breast cancer alert system can be developed using raspberry pie for real time application.
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