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A novel end-to-end deep learning approach for cancer detection based on microscopic medical images

Hammad Mohamed, Bakrey Mohamed, Bakhiet Ali, Tadeusiewicz Ryszard, Abd El-Latif Ahmed A., Pławiak Paweł

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

737--748

As a result of late diagnosis, cancer is the second leading cause of death in most countries in the world. Usually, many cases of cancer are diagnosed at an advanced stage, which reduces the chances of recovery from the disease due to the inability to provide appropriate treatment. The earlier cancer is detected, the more effective the treatment can be, especially for incurable cancers, which can result in a shorter life expectancy due to the rapid spread of the disease. The early detection of cancer also greatly reduces the financial consequences of it, as the cost of treating it in its early stages is much lower than in its other stages. Therefore, several previous studies focus on developing computer-aided cancer diagnosis systems (CACDs) that can detect cancer in its earliest stages automatically. In this paper, a novel approach is proposed for cancer detection. The proposed approach is an end-to-end deep learning approach, where the input images are fed directly to the deep model for final decision. In this research, the accuracy of a new deep convolutional neural network (CNN) for cancer detection is explored. The microscopic medical images obtained from the cancer database were used to evaluate our study, which were labelled as normal and abnormal images. The presented model achieved an accuracy of 99.99%, which is the highest accuracy compared with other deep learning models. Finally, the proposed approach would be very useful and effective, especially in low-income countries where referral systems for patients with suspected cancer are often unavailable, resulting in delayed and fragmented care.

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Faust O., Acharya U. R., Meiburger K. M., Molinari F., Koh J. E. W., Yeong C. H., Kongmebhol P., Ng K. H.

Biocybernetics and Biomedical Engineering

2018

Vol. 38, no. 2

275--296

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a compet-itive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which deliver real benefits to patients by improving the diagnosis accuracy while reducing health care cost.

System automatycznego wykrywania i lokalizacji raka stercza na obrazach perfuzyjnej tomografii komputerowej

Śmietański J., Tadeusiewicz R.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

2010

T. 14, z. 1

133-143

Obecnie stosowane procedury diagnostyczne w kierunku wykrycia bądź wykluczenia raka prostaty u mężczyzn są niewystarczające i często bywają zawodne. Nadzieję na zwiększenie skuteczności diagnozy w szczególnie trudnych przypadkach daje technika perfuzyjnej tomografii komputerowej. Metoda ta, będąca wciąż w fazie rozwoju, pozwala na pomiar parametrów przepływu krwi przez badaną tkankę, co uwidaczniane jest na barwnych dwuwymiarowych obrazach, tzw. "mapach parametrycznych". W pracy przedstawiono metodologię i algorytmy umożliwiające automatyzację interpretacji takich właśnie obrazów prostaty. Automatyzacja ta może nie tylko skrócić czas i zmniejszyć koszty diagnozy, ale przede wszystkim ułatwia podjęcie obiektywnej decyzji, niezależnej od subiektywnych ocen zależnych od doświadczenia czy indywidualnych właściwości wzroku diagnosty. Zaproponowana procedura została przetestowana na licznej grupie obrazów pochodzących od rzeczywistych pacjentów, a otrzymane rezultaty wskazują na możliwość stworzenia kompleksowego systemu pozwalającego na zwiększenie skuteczności i pewności stawianej diagnozy.

Detection and localization of the prostate cancer is difficult problem in general case. For this purpose the new method of medical imaging named perfusion computed tomography (p-CT) can be used. Nevertheless images registered by means of p-CT technology are difficult for interpretation, especially when interpretation must be earned by computer instead of experienced professional radiologist. In paper new algorithms for p-CT images automatic interpretation are presented and discussed. Using proposed algorithms both detection and localization of the prostate cancer can be performed. After general description of proposed methods illustrative case study is presented. For proper solution of the problem under consideration the original method for region of interest (ROI) localization is proposed. Such method named "life belt method" can be assessed as simple and effective and therefore it can be recommended for analysis of perfusion computed tomography prostate cancer images.