On the Dynamics and Feasibility of Transferred Inference for Diagnosis of Invasive Ductal Carcinoma: A Perspective

G M Harshvardhan,Pradeep Kumar Singh,Wei-Chiang Hong,Bunil Kumar Balabantaray,Aanchal Sahu,Vijander Singh,Mahendra Kumar Gourisaria

doi:10.1109/access.2022.3159700

Abstract

It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental to model performance in terms of validation accuracy and loss. Apart from vanilla CNNs, we have state-of-the-art (SOTA) architectures such as ResNet50 (and its variants) which show that through the use of skip-connections, higher performance metrics are attainable through deeper architectures. However, most evaluative metrics converge on a log scale as we go deeper with diminishing gradient of the metrics’ curves. Given these two contrasting speculations, in this paper, we implement various vanilla and SOTA CNNs for the diagnosis of one of the most common forms of breast cancer - invasive ductal carcinoma (IDC) - to examine and understand the feasibility of implementation of SOTA CNNs through transferred weights when juxtaposed with vanilla CNNs (and LeNet-5) of varying configurations in terms of their performance metrics and other parameters. In this paper, we solve the dual-objective of studying behavioural aspects of avant-garde CNN models (more specifically, VGG16, VGG19, ResNet50, ResNet50V2, MobileNetV2, and DenseNet121) and proper diagnosis of IDC through intermediate neural activations to critically evaluate and theorize the performance of different models. We notice that among all the models, only VGG16, VGG19, LeNet-5 and a selected vanilla CNN through an optimization procedure were the ones to attain the best metrics, shared amongst them.

Highlights

Deep Convolutional Neural Networks (CNNs) have interesting properties pertaining to the scalability of their feature capturing abilities
The rest of the paper is organized as follows – Section II (Related Work) describes the related work which is divided into three different techniques used majorly for the detection of Breast cancer (BCa), Section III (Methodology and Materials) we describe the nature of the data and the techniques used in this paper such as CNNs, transfer learning, etc., Section IV (Evaluation Strategy) in which we define briefly
We use pre-trained models namely VGG16, VGG19, ResNet50, MobileNetV2, DenseNet121 and ResNet50V2 along with LeNet-5 and a custom CNN architecture Cbest chosen by comparing various traditional small-scale CNNs through maximization of an optimization function

Summary

Introduction

Deep Convolutional Neural Networks (CNNs) have interesting properties pertaining to the scalability of their feature capturing abilities. The depth of the deep CNN is decided by the number of features, and both are directly proportional to one another. With the natural tendency of capturing features of all different levels, i.e., low, medium, and high [1], CNNs have been put to great use for various applications [2-6], inclusive of medical applications [2, 7, 8-12, 129]. The ImageNet is considered a standard benchmark for all SOTA models of object detection [14-17] and recognition. There are many categorizations of transfer learning as given by [20] such as instance-based, mapping-based, network-based and adversarial-based. Our implementation of transferred weights is a network-based approach where SOTA networks are pretrained on ImageNet over a plethora of images. We re-use these pre-trained architectures barring the last few layers

Methods

Results

Discussion

Conclusion