Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

Abstract

The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

Lungs nodule detection framework from computed tomography images using support vector machine

“…In the past few years, researchers have focused their attention on the development of automated tools and systems in the domain of computer vision that could detect and classify the anomalies in lesions in computed tomography (CT) and other imageries (Abbas et al, ; Abbas, Saba, Mohamad, et al, ; Abbas, Saba, Rehman, et al, ; M. A. Khan, Akram, Sharif, Awais, et al, ; M. A. Khan, Akram, Sharif, Javed, et al, ; M. A. Khan, Akram, Sharif, Shahzad, et al, ; Nasir et al, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman, Abbas, Saba, Rahman, et al, ; Saba et al, ; Yousaf et al, ). Majority of the previous research work has focused on the early detection of lungs cancer using the texture‐based interpretation of chest CTs (Reeves & Kostis, ).…”

“…A. Khan, Akram, Sharif, Awais, et al, 2018;M. A. Khan, Akram, Sharif, Shahzad, et al, 2018;Nasir et al, 2018;Rehman, Abbas, Saba, Mahmood, & Kolivand, 2018;Rehman, Abbas, Saba, Rahman, et al, 2018;Saba et al, 2019;Yousaf et al, 2019). Majority of the previous research work has focused on the early detection of lungs cancer using the texture-based interpretation of chest CTs .…”

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

“…To test the performance of the designed model, different brain tumor data sets have been used by researchers (Abbas et al, ; Abbas et al, ; Saba et al, ). Data sets published by Medical Image Computing and Computer‐Assisted Intervention (MICCAI) conference under Brain Tumour Segmentation (BRATS) Challenge have become the benchmark to test the model performance and make a comparison with other models.…”

“…To test the performance of the designed model, different brain tumor data sets have been used by researchers Abbas et al, 2018b;Saba et al, 2019) Fatima, Rehman, Almazyad, & Saba, 2017;Jamal et al, 2017;Mughal, Muhammad, et al, 2017a;Mughal, Muhammad, Sharif, Rehman, & Saba, 2018;Mughal, Sharif, et al, 2017b;Muhsin, Rehman, Altameem, Saba, & Uddin, 2014;Rad, Rahim, Rehman, Altameem, & Saba, 2013;Waheed et al, 2016). Such models include encoder-decoder models (Castillo et al, 2017;Saba, 2018;Saba, Rehman, & Sulong, 2011a;Sharif et al, 2017), multiple networks to extract the different level of features (Sedlar, 2017), models using convolutional networks with some postprocessing (Shaikh et al, 2017) and hybrid architectures with varying numbers of layers (Mundher, Muhamad, Rehman, Saba, & Kausar, 2014;Neamah, Mohamad, Saba, & Rehman, 2014;Saba & Alqahtani, 2013).…”

“…Brain cancer is among the different causes for an increase in mortality rate in the world. The death rate, due to a brain tumor, has been increased to 300% in the last few decades (Saba et al, ; Saba, Bokhari, Sharif, Yasmin, & Raza, ; Tahir et al, ). According to National Cancer Institute America (NCIA) and American Cancer Society (ACS), around 1.5 million cancer cases are identified in 2013 and more than 1.6 million cancer patients are registered in 2014 in America.…”