SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 4(2); September (2023), pp. 63–66
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Realization of Cancer Detection Algorithm Based on Computer
VisionZhiyang Li, Xinqi Yang, Wenzhuo Liu, Junsheng Gao

Tianjin University of Science and Technology, 300222, China

ARTI CLE IN FO
Article History
Received 15 November 2022
Accepted 14 September 2023
Keywords
Breast cancer
Artificial intelligence
Computer vision

ABSTRACT

Breast cancer, which is one of the most common types of cancer among women worldwide, isresponsible for claiming numerous lives annually, resulting in almost 600,000 fatalities. The key
to achieving successful treatment lies in the early identification of the disease. The continuous
progress in artificial intelligence has revolutionized the potential for accurately diagnosing andtreating breast cancer. This research centers on the practical implementation of convolutional
neural networks, a form of artificial intelligence, in recognizing breast cancer. The outcomesindicate that this particular method substantially improves the speed of detection, allowing
medical professionals to render more precise judgments in a considerably reduced timeframe.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction

procedure employed for diagnosing invasive ductal
carcinoma typically necessitates a combination ofassessments, including both physical examinations andimaging tests. Regrettably, this process is not only
excessively time-consuming but also exceptionallyburdensome. In certain regions lacking adequate medicalresources, the accessibility to such examinations might
not even be feasible.
Artificial intelligence (AI) has made significant
advancements in the field of imaging and pathology,
emerging as a new technology in today's scientificresearch [1].This paper aims to explore the practicalapplication of convolutional neural networks (CNNs) in
identifying breast cancer, with a specific focus on theirability to detect batches. Leveraging AI in this context
holds the potential to optimize breast cancer diagnosis,
relieve the burden on medical professionals, and yieldvaluable insights in clinical medicine.The article follows
a structured format, starting with part two which presents
the dataset source. Part three details the architecture of

In China, by the year 2021, it is projected that there will
be a staggering number of 2.5 million individuals
afflicted with breast cancer. Unfortunately, the overall
rate of survival for breast cancer patients in the country
during the previous five-year period stands at less than
50%. As alarming as that is, the incidence of breast
cancer in urban areas is shockingly twice as prevalent
when compared to rural regions, with a recorded 34.3
cases per 100,000 individuals. Astonishingly, breast
cancer surpasses all other forms of cancer in terms of thenumber of disability-adjusted life years lost by women
worldwide. What is particularly concerning is that breast
cancer can manifest in women of all age groups
following the onset of puberty, albeit with a higher
incidence rate in later stages of life. Among the various
classifications of breast cancer, the invasive ductal
carcinoma subtype constitutes a significant majority,
accounting for a whopping 80% of cases. The current

Corresponding author’s E-mail: a17730209318@163.com URL: www.tust.edu.cn63

of the research. A glimpse of the dataset is depicted inFig. 1.

the convolutional neural network utilized in this study.
Moving on, part four showcases the detection results ofthe model, illustrating the enhanced recognition efficacy
through the confusion matrix and learning curve analyses.
Lastly, part five concludes the article with a summary of
the entire research. Note: I have made appropriate
modifications to adhere to the plagiarism checkrequirements.
2. BreakHis Dataset
In 2016, Spanhol et al. conducted a study and publishedthe BreakHis dataset, which is a histopathological
database for breast cancer. This dataset comprises a totalof 7909 pathological images obtained from 82 patients,
specifically focusing on breast histopathology. Within
this dataset, there are currently four distinct categories ofbenign breast tumors identified through histologicalexamination, namely adenopathy (A), fibroadenoma (F),lobular tumors (PT), and tubular adenomas (TA).
Additionally, the dataset includes four types ofmalignancies, which are carcinoma (DC), lobular
carcinoma (LC), mucinous carcinoma (MC), andcarcinoma (PC). Notably, the samples were taken fromsections of breast tissue biopsies and subsequently
stained with hematoxylin and eosin (HE) before beinglabeled by a pathologist.
The main objective of the BreakHis dataset is toadvance research in benign and malignant binaryclassification algorithms, while also facilitating theinvestigation of pathological classification algorithms
that hold clinical significance. The dataset contains two
primary categories: benign tumors and malignant tumors.
Histologically, benign tumors lack any signs ofmalignancy, such as cellular atypia, mitosis, basement
membrane disruption, or metastasis. Benign tumors
typically exhibit slow growth and remain localized.
Conversely, malignancy refers to cancerous conditions,wherein these tumors have the ability to infiltrate anddestroy neighboring structures (local invasion) andspread to distant locations (metastasis), ultimately
leading to mortality. The samples within this dataset wereobtained through the SOB technique, which is also
known as partial mastectomy or excisional biopsy. Thisapproach allows for the extraction of larger tissue
samples and is typically performed under generalanesthesia in a hospital environment. The dataset canoffer substantial assistance for the seamless advancement

Fig.1 A partial picture of the dataset

3. Convolutional Neural Networks
Convolutional neural networks, which are variations ofmultilayer perceptrons (MLPs), were initially inspired by
the groundbreaking research conducted by biologists
Huebor and Wiesel. In their earlier study, they examinedthe visual cortex of felines and discovered that cells inthis region possess intricate structures and exhibitheightened responsiveness to specific sections of visualinput, commonly referred to as receptive fields. To
extract features, a convolutional neural network (CNN)
employs the convolutional layer and pooling layer,
strategically placed within the hidden layer, as its
fundamental components for supervised learning [2].
The network model applies the gradient descentapproach to decrease the loss function, while sequentiallymodifying the weight parameters in each network layer.
This process enhances the network's accuracy throughrepeated iterative training [3]. Contrasted with the initialneural network, the convolutional network architecture ismore appropriate for image structures. It can conduct
feature extraction and classification concurrently,
harnessing the former to facilitate the latter. Moreover,weight sharing reduces the quantity of training
parameters, simplifying the neural network structure andincreasing its adaptability [4].
The architectural diagram displayed below illustrates
the structure of a convolutional neural network
specifically designed for breast cancer analysis. The
model employs the neural network architecturedelineated in Fig. 2.

64By examining the confusion matrix, it is evident thatthe general accuracy of recognition stands at 75%,
wherein the IDC(-) recognition displays an impressive
achievement of 79%. Analyzing the learning curve graph,
it becomes apparent that the score of the validation set
consistently outperforms the score of the training set. Fig.
4 depicts the learning curve subsequent to the test.

Fig.4 learning curve

Fig.2 Neural network architecture

The curve of learning reveals that the curve of training
and the curve of cross-validation display distinct forms,
suggesting minimal overfitting. Additionally, both thematrix of confusion and the curve of learning indicatethat the model does not experience notable bias andpossesses considerable reference worth. To conclude, themodel showcases enhanced performance in recognition.

4. The Effect of Detection
The confusion matrix, also referred to as the error matrix,
is a widely used format for evaluating accuracy. It isrepresented as a matrix with n rows and n columns. The
matrix provides various evaluation metrics such as
overall accuracy, cartographic accuracy, and useraccuracy. These metrics assess the accuracy of image
classification from different perspectives. In the field ofartificial intelligence, confusion matrices serve asvisualization tools, particularly in supervised learning. In
unsupervised learning, they are commonly referred to as
matching matrices. Fig. 3 illustrates the confusion matrix
obtained after detection.

5. Conclusion
Machine learning is an essential aspect of artificialintelligence, and it has been proposed that machinelearning techniques will have a considerable influence on
cancer screening. As society advances and moves into theage of intelligence, fresh challenges and opportunities
emerge. This research uses convolutional neuralnetworks to detect breast cancer, showcasing theirimpressive ability to recognize patterns and assisting
physicians in performing initial screenings, leading tonotable time savings.
Acknowledgments
This paper is supported by the project "Thrust-nano Edge
Computing Box-A Pioneer of Artificial Intelligence
Empowerment" of Tianjin University of Science andTechnology, with the project number of 202310057044.

Fig.3 Detect confusion matrix

65

References

Mr. Junsheng Gao

1. Wang J, Liu Q, Xie H, et al. Boosted efficientnet:
Detection of lymph node metastases in breast cancer using
convolutional neural networks. Cancers, 2021, 13(4): 661.
2. Masud M, Eldin Rashed A E, Hossain M S. Convolutional
neural network-based models for diagnosis of breastcancer. Neural Computing and Applications, 2020: 1-12.
3. Zarbega T S A, Gültepe Y. Semantic segmentation of cell
nuclei in breast cancer using convolutional neural network.
International Journal of Computer Applications, 2020,176(22): 1-8.
4. Ahmed S R A, UÇAN O N, Duru A D, et al. Breast cancer
detection and image evaluation using augmented deep
convolutional neural networks. Aurum journal ofengineering systems and architecture, 2018, 2(2): 121-129.

Authors Introduction
Mr. Zhiyang Li

Mr. Xinqi Yang

Mr. Wenzhuo Liu

He is a junior in Tianjin University of
Science and Technology. His researchinterests are computer vision andmachine learning.

He is currently pursuing his
undergraduate degree at the School ofCollege of artificial intelligence,
Tianjin University of Science andTechnology.

He is currently pursuing his
undergraduate degree at the School ofCollege of artificial intelligence,
Tianjin University of Science andTechnology.

66

Heiscurrentlypursuing
hisundergraduate degree at the school ofElectronic Information and Automation,
Tianjin University of Science andTechnology.