A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Doreswamy,; M, Umme Salma

doi:10.5121/ijscai.2016.5301

Cited by 10 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study by Doreswamy et al [10], an improved method based on the Bat algorithm was presented for classification of breast cancer. In this study, binary Bat algorithm was used.…”

Section: Previous Methodsmentioning

confidence: 99%

Breast Cancer Detection Improvement by Grasshopper Optimization Algorithm and Classification SVM

Rahmani¹,

Katouli²

2020

RIA

View full text Add to dashboard Cite

Given that breast cancer is one of the most difficult and dangerous cancers, the use of diagnostic methods in the early stages of its development can be very effective and important in the process of treating patients. This early diagnosis can help doctors treat patients, thus greatly reducing mortality. Many different features have been collected to diagnose and predict breast cancer, and it is very difficult for specialists to use all of these features for a large number of cancers. The aim of this study is to provide a new method for minimizing the process of breast cancer diagnosis through the Grasshopper optimization algorithm. The steps of the proposed method consist of three main parts: The first step after receiving the data is to normalize the pre-processed data. The second step is to reduce the features using the GOA. The final step is to select the optimal features and improve the parameters using the SVM Classifier. The experiments in this study were performed on three datasets, namely WBC (Wisconsin Breast Cancer), WDBC (Wisconsin Diagnosis Breast Cancer) and WPBC (Wisconsin Prognosis Breast Cancer). The results show that the accuracy of the proposed method is 99.51, 98.83 and 91.38 for the WBC, WDBC and WPBC datasets, respectively. In comparison with other methods, the results show that the proposed method has better performance.

show abstract

“…In a study by Doreswamy et al [10], an improved method based on the Bat algorithm was presented for classification of breast cancer. In this study, binary Bat algorithm was used.…”

Section: Previous Methodsmentioning

confidence: 99%

Breast Cancer Detection Improvement by Grasshopper Optimization Algorithm and Classification SVM

Rahmani¹,

Katouli²

2020

RIA

View full text Add to dashboard Cite

show abstract

“…As samples are selected at random, selection of features was sometimes difficult. In (Doreswamy, 2016), Dore swamy et al improved Bat algorithm to classify breast cancer images. 569 samples of UCI data were involved in experimenting this method.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Breast Cancer Detection Using WPSO

Doma¹,

Padmanandam²,

Tambvekar

et al. 2022

International Journal of Operations Research and Information Systems

View full text Add to dashboard Cite

To detect breast cancer in the early stages, microcalcifications are considered a key symptom. Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used. Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment. This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying microcalcifications as normal, benign, or malignant, thereby improving the accuracy. In the breast region, tumor part is extracted using optimization methods. Here, artificial intelligence (AI) is proposed for detecting breast cancer, which reduces the manual overheads. AI framework is constructed for extracting features efficiently. This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal. This model uses MG images obtained from hospitals.

show abstract

“…As samples are selected at random, selection of features was sometimes difficult. In [12], Dore swamy et al improved Bat algorithm to classify breast cancer images. 569 samples of UCI data were involved in experimenting this method.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction based Breast Cancer Detection using WPSO with CNN

Ponnaganti¹,

Anitha²

2021

IJACSA

View full text Add to dashboard Cite

The cancer reports of the past few years in India says that 30% cases have breast cancer and moreover it may increase in near future. It is added that in every two minutes, one woman is diagnosed and one expires in every nine minutes. Early diagnosis of cancer saves the lives of the individuals affected. To detect breast cancer in early stages, micro calcifications is considered as one key symptom. Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used. Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment. This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying micro calcifications as normal, benign or malignant thereby improving the accuracy. In the breast region, tumour part is extracted using optimization methods. Here, Convolutional Neural Networks (CNNs) is proposed for detecting breast cancer which reduces the manual overheads. CNN framework is constructed for extracting features efficiently. This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal. This model uses MG images which were obtained from various local hospitals.

show abstract

A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Cited by 10 publications

References 15 publications

Breast Cancer Detection Improvement by Grasshopper Optimization Algorithm and Classification SVM

Breast Cancer Detection Improvement by Grasshopper Optimization Algorithm and Classification SVM

Artificial Intelligence-Based Breast Cancer Detection Using WPSO

Feature Extraction based Breast Cancer Detection using WPSO with CNN

Contact Info

Product

Resources

About