A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

Abstract

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.

Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer

“…Training the model (i) Perform bootstrap process on the training data (ii) Choose the number of clusters to be formed based on the data (iii) Apply a classification algorithm like a decision tree (5) Evaluate the model on the test set (i) Compute the accuracy from each cluster in the model and average the accuracy (ii) Determine the output prediction by a voting process (6) Stop Mathematical Problems in Engineering used for logistic regression was selected based on the grid search method. ree solvers "newton-cg," "liblinear," and "saga" were used and the learning rate was set in a range from 0.001 to 10.…”

“…Training the data (i) Select "i" random features from the entire set of input features i (ii) Determine the nodes from the "i" features using the best split point ( 4) Repeat (i) e above three steps until a speci c number of nodes are reached ( 5) Repeat (i) e above steps for n times to create m decision trees (6) Testing the data (i) Feed-in a new data sample and predict the output label (ii) e node which gives high prediction among the other output nodes will be taken as the nal prediction (7) Stop where H(s) is the final output prediction and h i (s) is the output prediction for each sample in a classifier.…”

“…ere are many works done by researchers working in these areas and these kinds of works give rise to the establishment of smart hospitals and constructive exploitation of these technologies leads to the improvement in quality of life [5]. Chandrasekaran et al [6] developed a fully integrated analog-based machine learning classi er that uses Arti cial Neural Network (ANN) algorithms for breast cancer prediction. e developed ANN model mainly focused on nine attributes of breast cancer, such as (1) bland chromatin, (2) mitosis, (3) uniformity of cell shape, (4) marginal adhesion, (5) single epithelial cell size, (6) uniformity of cell size, (7) clump thickness, (8) normal nucleoli, and (9) bare nuclei.…”