Comparative Analysis of Artificial
Intelligence-Based Algorithms for Early Detection of Breast Cancer
Pedro Narciso Delgado Chávez
Universidad Técnica de Manabí
Portoviejo, Ecuador
Roberth Abel Alcívar Cevallos
Universidad Técnica de Manabí
Portoviejo, Ecuador
DOI: https://doi.org/10.56124/encriptar.v9i17.004
ABSTRACT
Breast cancer is one of the leading causes of
mortality among women worldwide, representing a major public health problem.
Early detection is crucial for increasing survival rates, since establishing a
timely diagnosis and applying the appropriate treatment can help stop the
disease in its early stages. In this context, the use of algorithms based on
artificial intelligence has become a highly relevant tool in the field of early
diagnosis, particularly within medical oncology. Several algorithms have demonstrated
great potential in accurately classifying medical images, including Support
Vector Machines (SVM), Random Forests, Multilayer Perceptrons (MLP), and
Convolutional Neural Networks (CNN). This approach enables a more detailed and
reliable analysis of medical imaging studies (mammograms, ultrasounds, computed
tomography, and magnetic resonance imaging), facilitating the early detection
of malignant lesions and reducing diagnostic errors. The main objective of this
research was to conduct a comparative analysis of artificial intelligence-based
algorithms and evaluate, through metrics such as accuracy, sensitivity,
specificity, F1-score, and the area under the curve (AUC), which algorithm
performs best in the early detection of the disease. Consequently, the
integration of artificial intelligence algorithms into clinical decision
support systems contributes significantly to improving medical decision-making,
ensuring that treatments are more appropriate, and providing a better quality
of life for patients diagnosed with the disease.
Keywords: Artificial
Intelligence, Breast Cancer, Ultrasound, Machine Learning, Deep Learning.
Análisis
comparativo de algoritmos basados en inteligencia artificial para la
detección temprana del cáncer de mama
Resumen
El cáncer de mama constituye una de las principales
causas de mortalidad en mujeres a nivel mundial, representando un gran problema
de salud pública. La detección temprana resulta determinante para incrementar
las tasas de supervivencia, ya que al establecer un diagnóstico oportuno y
poder aplicar el tratamiento adecuado que favorezca aparar la enfermedad en sus
fases tempranas (estadio).Este contexto, de emplear los algoritmos basados en
inteligencia artificial se ha convertido en una herramienta de gran relevancia
en el campo del diagnóstico temprano en este caso en la rama médica de la
oncología. Diversos algoritmos han demostrado un gran potencial en la
precisión, en la clasificación de la imágenes médicas entre ellos se tiene la
Máquina de vectores de soporte (SVM), Bosques aleatorios(Random Forest)
,Perceptron multicapa (MLP) y Redes
neuronales convolucionales(CNN).Este enfoque permite realizar un análisis más
detallado y confiable de estudios de imágenes médicas (mamografías, ecografías,
tomografías y resonancias magnéticas) dando facilidad para la detección
temprana de lesiones malignas y reducir diagnósticos erróneos. El objetivo
fundamental de la investigación fue realizar una comparativa de los algoritmos
basados en inteligencia artificial y medir a través de las métricas como la
precisión ,sensibilidad, especificidad ,f1-score y el área bajo la curva(AUC),
cual es el algoritmo con mejor rendimiento para la detección temprana de la
enfermedad . En consecuencia, la integración de algoritmos basados en
inteligencia artificial en los sistemas de apoyo clínico contribuye de manera
significativa a mejorar las decisiones médicas y que el tratamiento sea el más adecuado y se brinde
una mejor calidad de vida de las
pacientes diagnosticadas con la enfermedad.
Palabras clave: Inteligencia Artificial, Cáncer de mama,
Ultrasonido, Aprendizaje Automático, Aprendizaje Profundo.
1.
Introduction
Breast cancer is one of the diseases with the highest mortality rate
among women worldwide and represents one of the leading causes of death in the
female population. Early detection of breast cancer is essential to improve the
chances of effective treatment and to increase survival rates. However, early
identification of the disease has its limitations due to the variability in the
presentation of lesions in ultrasound images.
Artificial
intelligence is a branch of computer science that encompasses concepts related
to learning, logical reasoning, and autonomous decision making. It focuses on
the design and development of computational systems capable of learning from
data, reasoning logically, and self-correcting their processes, thereby
enabling the simulation of aspects of human intelligence.
In recent years, artificial intelligence
(AI) has shown significant progress in the field of medicine, particularly in
the interpretation of medical images. The advances in Artificial Intelligence
(AI) are rapid, comprehensive, and increasingly effective. There is controversy
regarding the acceptance of computerized breast cancer detection, with patient
trust on one hand and the competitiveness of the algorithm on the part of the
medical community. For this reason, algorithms such as Support Vector Machines
(SVM), Random Forest, Multilayer Perceptron (MLP), and Convolutional Neural
Networks (CNN) have been employed.
The main objective of the study is to
identify which of the evaluated algorithms offers the best performance in the
early detection of breast cancer. For the comparison, accuracy, sensitivity,
specificity, F1 score, and area under the curve (AUC) were considered as
reference metrics, using a set of ultrasound images. The evaluation of the
algorithms allows for the identification of their strengths and weaknesses,
which provides relevant information for their application in clinical decision
support systems.
2. Methodology
This
research was supported by prior theoretical and practical knowledge previously
established in the fields of machine learning and deep learning. The study is
applied in nature, as it sought to solve a practical problem by evaluating
machine learning and deep learning algorithms, addressing a real-world issue.
It is also experimental, since simulations and tests were conducted on
artificial intelligence–based algorithms, allowing the observation of results
that, from the outset, show expected results.
2.1 Methods
This
article employed three methods: quantitative, qualitative, and comparative. The
quantitative approach used key metrics such as accuracy, sensitivity,
specificity, and the area under the curve (AUC) to evaluate the algorithms'
performance in detecting patterns in medical images(mammograms, ultrasound, and
magnetic resonance imaging).
Within the
quantitative approach, the variables normal, benign, and malignant were used to
evaluate the effectiveness of artificial intelligence–based algorithms. The use
of these variables allowed for better data adjustment, reducing image quality
loss and minimizing the likelihood of less efficient outcomes, such as false
positives and true negatives. The main objective of the testing phase was to
ensure that the results were efficient, thereby increasing the reliability of
the algorithms.
The
comparative method made it possible to determine which algorithm offers the
best performance in the early detection of breast cancer, considering aspects
such as diagnostic accuracy, training time, and overall performance. By
applying the three methods, each detail was analyzed and carefully considered
in the metrics and variables to obtain reliable results.
2.2 Information
Collection Techniques
Among the
techniques employed in this research, an exhaustive literature review was
conducted on academic-scientific articles, academic journals, and systematic
reviews concerning artificial intelligence–based algorithms for breast cancer
detection, using academic databases such as Scopus, IEEE Xplore, ResearchGate,
Google Scholar, Science Direct, and PubMed.
2.3 Tools and Resources.
As part of the development and modeling
tools, the artificial intelligence algorithms used in this research were
designed, trained, tested, and compared. Through these stages of the work,
improvements in each of the algorithms could be observed.
2.4
Technological Equipment
A computer
system was used with the following hardware specifications: an 11th-generation
Intel Core i5 processor at 4.2 GHz, 8 GB of RAM, a 521 GB solid-state drive,
and an NVIDIA TX 9800 graphics card. At the software level, Windows 11 Single
Language was employed, along with Google Collaboration, a cloud-based
environment running Python 3.10 and providing GPU (Graphics Processing Unit)
support for image data processing. The operating system (Windows 11) was
complemented with Keras 3.0 as the standard library for image processing tasks
and Tensor Flow version 2.13, which operates in conjunction with Python.
2.5 Phases of the Research Work
2.5.1 Planning and Data Collection
An exhaustive literature review was
conducted on artificial intelligence–based algorithms for breast cancer
detection. Additionally, the dataset for the research was sourced from
recognized repositories such as Kaggle, among others.
2.5.2 Data Preprocessing
The dataset
was collected and analyzed, with particular attention given to its structure in
order to determine the variables most relevant for this study. It consisted of
clinical images (mammograms and ultrasounds). For the purposes of this
research, ultrasound images were selected, and the Support Vector Machine (SVM)
algorithm was initially applied. Key features were extracted to obtain
meaningful data, which were then processed and refined to ensure consistency
and reliability.
The Breast
Ultrasound Images (BUSI) dataset was used, comprising 1,578 images in total:
266 classified as normal, 891 benign, and 421 malignant. The quality and
suitability of the images for algorithmic processing were carefully reviewed.
When necessary, adjustments were made to attributes such as brightness,
contrast, weight, and dimensions, which facilitated the preprocessing stage and
improved the dataset’s usability. Once this process was completed, the dataset
was prepared for the training and evaluation phases of the selected algorithms.
To further improve image quality and achieve more optimal results, additional
preprocessing techniques were applied using Python’s OpenCV (cv2) library, in
combination with the CLAHE (Contrast Limited Adaptive Histogram Equalization)
method, implemented locally.
The data
were divided into 70% for training, 15% for validation, and 15% for testing.
The hyperparameters were configured as follows: for SVM, the RBF kernel
function; for Random Forest, 100 decision trees with a fixed seed of 42; for
the Multilayer Perceptron (MLP), three hidden layers (128, 64, 32); and for the
Convolutional Neural Networks (CNN), three convolutional layers with ReLU
activation, 10 epochs, and the Adam optimizer.In addition, k-fold
cross-validation was performed, with the number of folds ranging between 5 and
10. After these procedures, the models were trained and evaluated, and their
performance metrics were compared to identify the most effective algorithm.
It should
be noted that the BUSI dataset is organized into three subdirectories: normal,
benign, and malignant, which served as the basis for classification into these
three categories throughout the study. In the appendix, the raw images and the
processed images are presented.
2.5.3
Algorithm Training and Evaluation
In this phase of the research, where the
algorithms were trained and evaluated, the Breast Ultrasound Images Dataset
(BUSI) was selected, which contains 1,578 images distributed across the
categories normal, benign, and malignant. Equal conditions were granted to all
algorithms during training and evaluation, as they were tested on the same
dataset. The Support Vector Machine (SVM) algorithm was evaluated first, since
it directly extracts relevant features from medical images (such as ultrasound
and mammograms), including dimension, texture, weight, and shape, and adapts
them to work more efficiently.
Next, the Random Forest algorithm was
evaluated, which bases its operation on making the best decision by working
with multiple decision trees. Similar to SVM, it transforms these features into
a feature vector of the images and evaluates them as a classifier. Each
decision tree casts a vote for a class, and the final result is determined
either by majority voting or by averaging the outputs of the trees. Subsequently,
the Multilayer Perceptron (MLP) algorithm was assessed.
This artificial neural network is composed
of layers of nodes (neurons). When evaluated with medical images, it processes
the data through its layers, producing as output a class value: normal, benign,
or malignant. As the data passes through the iterations of its hidden layers,
the algorithm ultimately selects the class determined by its programmed logic.
Finally, the Convolutional Neural Network (CNN) algorithm was evaluated. CNNs
operate directly on images by automatically detecting relevant spatial patterns
through the use of convolutions.
This study constitutes a comparative
analysis of the aforementioned algorithms; therefore, their performance was
measured using quality metrics such as accuracy, sensitivity, specificity,
F1-score, and area under the curve (AUC), in order to determine which algorithm
performs best in the processing of medical images (ultrasound).
3. Results
and Discussion
3.1 Results
The results
obtained from the comparative analysis of the algorithms employed in this
research for the early detection of breast cancer were derived using the Breast
Ultrasound Images (BUSI) dataset, which contains a total of 1578 images (266
normal, 891 benign, and 421 malignant).Each algorithm was trained and
evaluated, and confusion matrices were generated along with the specified
metrics, including accuracy, sensitivity (recall), specificity, F1 score, and
area under the curve (AUC).
The
simplified confusion matrices for each algorithm are presented below, based on
the medical images dataset, with 15% of the data
reserved for validation, as shown in Figures 1, 2, 3, and 4.
Additionally, a comparative data table and a graph of
errors in accuracy and loss experienced by the algorithms are shown in Table 3
and Figure 11:
Figure 1. Confusion Matrix of the Support Vector Machine (SVM) Algorithm
Note: Pedro Delgado (2025).
Figure
2. Confusion Matrix of the
Random Forest Algorithm
Note: Pedro Delgado (2025).
Figure
3. Confusion Matrix of
the Multilayer Perceptron (MLP) Algorithm
Note: Pedro Delgado (2025).
Figure
4. Confusion Matrix of the
Convolutional Neural Networks (CNN) Algorithm.
Note: Delgado Pedro (2025).
3.2 Discussion
This
section of the article presents a comparative table of the algorithms evaluated
in terms of efficiency and performance, along with the metrics of accuracy,
sensitivity (recall), F1 score, and area under the curve (AUC).An additional
column was included to report the training time, and a graph was provided with
the corresponding values obtained in the study.
The
results show that Convolutional Neural Networks (CNN) achieved the best overall
performance (accuracy of 88%, AUC = 0.92), hich has been observed in previous
studies that highlight their effectiveness in the classification of medical images.
The Multilayer Perceptron (MLP) achieved a sensitivity of 86%, which is a good
result but could have limitations when computational resources are low.
The
Support Vector Machine (SVM) and Random Forest algorithms showed intermediate
performance, with accuracies close to 74-76% and an AUC of 0.81 for SVM and
0.85 for Random Forest. These findings suggest that the algorithms should be
adapted to clinical needs: CNN for greater accuracy, MLP when the minimization
of false negatives is a priority, and SVM or Random Forest when a balance
between performance and computational simplicity is required. The following
table and graph illustrate the results described in this discussion.
Table 1. Comparison of metric values and the respective
algorithms used
|
Algorithm |
Accuracy |
Sensitivity
(Recall) |
Specificity |
F1-Score |
AUC |
Training Time (min) |
|
SVM |
0.74 |
0.73 |
0.81 |
0.71 |
0.81 |
2 |
|
RF |
0.76 |
0.80 |
0.80 |
0.73 |
0.85 |
3 |
|
MLP |
0.84 |
0.86 |
0.85 |
0.75 |
0.90 |
10 |
|
CNNs |
0.88 |
0.86 |
0.90 |
0.78 |
0.92 |
20 |
Note: Pedro Delgado (2025).
Figure 5. Accuracy, Sensitivity, Specificity, F1-Score, AUC, and
Training Time Metrics
Note: Pedro Delgado (2025).
In the discussion
section, the area under the curve (AUC) graphs are presented to provide a
clearer view of how the results of each evaluated algorithm were validated.
Figure
6. Support Vector Machine
(SVM) Algorithm – AUC Curve
Note: Pedro Delgado (2025).
Figure
7. Random Forest Algorithm
– AUC Curve
Note: Pedro Delgado (2025).
Figure 8. Multilayer
Perceptron (MLP) – AUC Curve
Note: Pedro Delgado (2025).
Figure
9. Convolutional
Neural Networks (CNN) Algorithm – AUC Curve
Note: Pedro Delgado (2025).
In the table
two (2), shows the values obtained for each class: normal, benign, and
malignant, after the evaluation of each algorithm used in the research:
Table
2. Comparison of Algorithms
Based on the Area under the Curve (AUC) Metric.
|
Algorithms |
Normal |
Bening |
Malignant |
|
SVM |
0.92 |
0.76 |
0.84 |
|
Random Forest |
0.96 |
0.83 |
0.85 |
|
MLP |
0.95 |
0.93 |
0.94 |
|
CNNs |
0.87 |
0.86 |
0.97 |
Note: Pedro Delgado (2025).
Figure 10. Bar Chart of the Algorithms Compared Using the Area Under the Curve
(AUC) Metric.
Note: Pedro Delgado (2025).
Table
3. The accuracy and loss values are shown for each of the
algorithms.
|
Algorithms |
Accuracy |
Loss |
|
SVM |
0.74 |
0.26 |
|
RANDOM FOREST |
0.76 |
0.24 |
|
MLP |
0.84 |
0.16 |
|
CNN |
0.88 |
0.12 |
Note: Pedro Delgado (2025).
Figure
11. Graph of the accuracy and losses in the evaluated
algorithms.
Note: Pedro Delgado (2025).
5. -
Conclusions
A 2024
study by Zou et al, on breast cancer prediction, which utilized various
algorithms, including the Support Vector Machine (SVM) and Random Forest (RF),
demonstrated that these algorithms perform well with binary datasets like the
Wisconsin dataset. In contrast, when applied to the ultrasound breast image
dataset used in the comparative analysis article on AI-based algorithms for
early breast cancer detection, SVM and RF achieved acceptable, yet lower,
accuracy scores of 0.74 and 0.76, respectively. This finding suggests that while
these algorithms may not be optimally suited for direct medical image analysis,
they can still be effectively employed on datasets with similar underlying
features.
Furthermore,
other studies have highlighted that the Multilayer Perceptron (MLP) and
Convolutional Neural Networks (CNNs) are powerful tools in the field of medical
image diagnostics. In this specific study, they yielded superior results, with
an accuracy of 0.84 for MLP and 0.88 for CNNs.
Beyond the
standard accuracy metric, another critical performance measure is the Area
under the Curve (AUC-ROC). This metric, which evaluates how well the algorithm
classifies each category within the medical image dataset, also showed strong
results. CNNs demonstrated exceptional capability, leading with an AUC of 0.97
for the malignant class, 0.86 for the benign class, and 0.87 for the normal class.
Based on these comparative values and metrics, it can be concluded that
Convolutional Neural Networks (CNNs) are the superior algorithm when compared
to SVM, Random Forest, and MLP for this specific task of early breast cancer
detection using medical images.
Appendix
A
B
In the
following figures, we present a benign-class image prior to preprocessing
(Figure A). In contrast, Figure B shows the same image after undergoing
preprocessing using the CLAHE technique (Contrast Limited Adaptive Histogram
Equalization).
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