Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images

Abstract

Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body; this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it is measured by providing patients with the essential treatment so their efficient lifetime can be protected. Scientists and researchers in numerous studies have initiated techniques to identify tumours in early phases. Still, misperception in classifying skeptical lesions can be due to poor image excellence and dissimilar breast density. BC is a primary health concern, requiring constant initial detection and improvement in analysis. BC analysis has made major progress recently with combining multi-modal image modalities. These studies deliver an overview of the segmentation, classification, or grading of numerous cancer types, including BC, by employing conventional machine learning (ML) models over hand-engineered features. Therefore, this study uses multi-modality medical imaging to propose a Computer Vision with Fusion Joint Transfer Learning for Breast Cancer Diagnosis (CVFBJTL-BCD) technique. The presented CVFBJTL-BCD technique utilizes feature fusion and DL models to effectively detect and identify BC diagnoses. The CVFBJTL-BCD technique primarily employs the Gabor filtering (GF) technique for noise removal. Next, the CVFBJTL-BCD technique uses a fusion-based joint transfer learning (TL) process comprising three models, namely DenseNet201, InceptionV3, and MobileNetV2. The stacked autoencoders (SAE) model is implemented to classify BC diagnosis. Finally, the horse herd optimization algorithm (HHOA) model is utilized to select parameters involved in the SAE method optimally. To demonstrate the improved results of the CVFBJTL-BCD methodology, a comprehensive series of experimentations are performed on two benchmark datasets. The comparative analysis of the CVFBJTL-BCD technique portrayed a superior accuracy value of 98.18% and 99.15% over existing methods under Histopathological and Ultrasound datasets.

Keywords: Breast cancer; Computer vision; Horse Herd Optimization Algorithm; Image preprocessing; Transfer learning.

Fig. 1

Overall flow of CVFBJTL-BCD approach.

Fig. 2

Workflow of GF technique.

Fig. 3

Framework of DenseNet201.

Fig. 4

Architecture of InceptionNetV3 model.

Fig. 5

Structure of MobileNetV2 model.

Fig. 6

SAE framework.

Fig. 7

Structure of HHOA model.

Fig. 8

Sample Images (a) Histopathological Images (b) Ultrasound Images.

Fig. 9

Histopathological dataset (a-b) Confusion matrices and (c-d) PR and ROC curves.

Fig. 10

Average of CVFBJTL-BCD technique on Histopathological dataset.

Fig. 11

$Acc{u}_y$ curve of CVFBJTL-BCD technique on Histopathological dataset.

Fig. 12

Loss curve of CVFBJTL-BCD technique on Histopathological dataset.

Fig. 13

$Acc{u}_y$ and $Pre{c}_n$ analysis of CVFBJTL-BCD technique on Histopathological dataset.

Fig. 14

$Sen{s}_y$ and $Spe{c}_y$ analysis of CVFBJTL-BCD technique on Histopathological dataset.

Fig. 15

CT analysis of CVFBJTL-BCD technique on Histopathological dataset with existing models.

Fig. 16

Ultrasound Dataset (a-b) Confusion matrices and (c-d) PR and ROC curves.

Fig. 17

Average of CVFBJTL-BCD technique on Ultrasound dataset.

Fig. 18

$Acc{u}_y$ curve of CVFBJTL-BCD technique on Ultrasound dataset.

Fig. 19

Loss curve of CVFBJTL-BCD technique on Ultrasound dataset.

Fig. 20

$Acc{u}_y$ and $Pre{c}_n$ analysis of CVFBJTL-BCD technique on Ultrasound dataset.

Fig. 21

$Sen{s}_y$ and $Spe{c}_y$ analysis of CVFBJTL-BCD technique on Ultrasound dataset.

Fig. 22

CT evaluation of the CVFBJTL-BCD technique on Ultrasound dataset with existing methods.