Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

Ansar, Wajeeha; Raza, Basit; Dar, Amir H.

doi:10.1007/978-3-030-43364-2_2

Cited by 34 publications

(20 citation statements)

References 19 publications

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“…In this paper [ 32 ], an author proposed a method for the detection of breast cancer tumors using Mobile Net on DDSM and CBIS-DDSM datasets. This method achieved 74.5% accuracy, 76% sensitivity, and 70% precision for CBIS-DDSM dataset and 86.8% accuracy, 95% sensitivity for DDSM dataset but FNR value for CBIS-DDSM dataset increased to 24%.…”

Section: Related Workmentioning

confidence: 99%

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

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Section: Related Workmentioning

confidence: 99%

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Moreover, the results showed that the segmentation accuracy increased to 73.6% when using samples from the CBIS-DDSM dataset, furthermore, the classification accuracy enhanced to became 87.2% with an AUC of 94%. [12] presented a MobileNet based architecture model that was able to classify the masses in the mammograms into malignant and benign with competitive performance relative to the state of art architectures and less computational cost. The proposed approach firstly detects the masses in the mammogram through classifying the mammograms into cancerous and non-cancerous using a CNN, then the cancerous ones are fed into a pretrained MobileNet based model to be classified.…”

Section: Transfer Learningmentioning

confidence: 99%

Mammogram breast cancer CAD systems for mass detection and classification: a review

Hassan

Hamad

Mahar

2022

Multimed Tools Appl

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Although there is an improvement in breast cancer detection and classification (CAD) tools, there are still some challenges and limitations that need more investigation. The significant development in machine learning and image processing techniques in the last ten years affected hugely the development of breast cancer CAD systems especially with the existence of deep learning models. This survey presents in a structured way, the current deep learning-based CAD system to detect and classify masses in mammography, in addition to the conventional machine learning-based techniques. The survey presents the current publicly mammographic datasets, also provides a dataset-based quantitative comparison of the most recent techniques and the most used evaluation metrics for the breast cancer CAD systems. The survey provides a discussion of the current literature and emphasizes its pros and limitations. Furthermore, the survey highlights the challenges and limitations in the current breast cancer detection and classification techniques.

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“…Then, research conducted by Suharto et al,(2020) which also applied MobileNets-V1 architecture, to classify freshwater fish species has successfully obtained an accuracy of 90.00% [7]. MobileNets-V1 has also been used to detect diseases, which is done by Ansar et al to detect breast cancer with an accuracy score of 86.8% [8]. Therefore, in this research we apply MobileNets-V1 to classify fish.…”

Section: Introductionmentioning

confidence: 96%

MobileNets-V1 Architecture for Web Based Fish Image Classification

Prasetyo

Hogantara

Isnainiyah

2021

Data Science: J. of Computing and Appl. Informatics

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Recently, the research study about fish identification become a very challenging to researchers. Climate and environmental changes have a major impact on fish species and their environment. To identify fish using manual process is time consuming and need effort to gather samples in different environment. The identification of fish species is performed by using feature extraction and a series of features. Generally, the characteristic is divided into two groups namely general characteristics and anatomical features. General characteristics is characteristic that can be seen directly without the aid of tools. The characteristics include color, texture, and fiber direction. Although, manual is performed by expert but is possible that identification is not accurate. Therefore, to overcome the problem, we create a web-based application for identifying fish by using image as input. We use 10 class data with 300 images for each class. Then, we split into training and testing with 80:20 ratio. The application was developed by using the MobileNets- V1 model. The proposed method has accuracy on 89 %, that obtain from training score is 91.04%, validation is 88,96%. This score is higher than other methods that used in this application. Total time for computation process is about 127 minutes.

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Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

Cited by 34 publications

References 19 publications

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mammogram breast cancer CAD systems for mass detection and classification: a review

MobileNets-V1 Architecture for Web Based Fish Image Classification

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