D’OraCa: Deep Learning-Based Classification of Oral Lesions with Mouth Landmark Guidance for Early Detection of Oral Cancer

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Classification of retinal images with deep learning for early detection of diabetic retinopathy disease

2018 26th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2018.8404369 ◽

2018 ◽

Cited By ~ 3

Author(s):

Nursel Yalcin ◽

Seyfullah Alver ◽

Necla Uluhatun

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Early Detection ◽

Retinal Images

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Comparison of Object Detection Approaches Applied to Field Images of Papanicolaou Stained Cytology Slides

10.1101/2021.08.25.21262605 ◽

2021 ◽

Author(s):

André Victória Matias ◽

Allan Cerentini ◽

Luiz Antonio Buschetto Macarini ◽

João Gustavo Atkinson Amorim ◽

Felipe Perozzo Daltoé ◽

...

Keyword(s):

Cervical Cancer ◽

Deep Learning ◽

Oral Cancer ◽

Global Health ◽

Object Detection ◽

Learning Approach ◽

Non Invasive ◽

Global Health Issue ◽

Invasive Method

Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, that can also be useful to detect cancer on oral cavities. Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. The manual process of analyzing cells to detect abnormalities is time-consuming and subject to variations in perceptions from different professionals. To evaluate a possible solution to the automation of this process, in this paper we employ the object detection deep learning approach in the analysis of this type of image using 3 models: RetinaNet, Faster R-CNN, and Mask R-CNN. We trained and tested the models using images from 6 cytology slides (4 cancer cases and 2 healthy samples) and our results show that Mask R-CNN was the best model for localization and classification of nuclei with an IoU of 0.51 and recall of abnormal nuclei of 0.67.

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Frühzeitige Detektion von Oberflächenzerrüttungen/Premature detection of surface disruption – Deep Learning-based method for classification of damage on ball screw drives

wt Werkstattstechnik online ◽

10.37544/1436-4980-2020-07-08-45 ◽

2020 ◽

Vol 110 (07-08) ◽

pp. 501-506

Author(s):

Peter Ruppelt ◽

Tobias Schlagenhauf ◽

Jürgen Fleischer

Keyword(s):

Deep Learning ◽

Early Detection ◽

Preventive Maintenance ◽

Machine Tools ◽

Industry 4.0 ◽

Industrie 4.0 ◽

Ball Screw ◽

Camera System ◽

Ball Screw Drives

Die Zustandsüberwachung von Anlagen, Maschinen und deren Bauteilen ist eine zentrale Thematik von Industrie 4.0. Unvorhergesehene Ausfälle von Werkzeugmaschinen sind häufig auf den Verschleiß und das daraus resultierende Versagen von Kugelgewindetrieben zurückzuführen. Aufgabe dieser Arbeit ist die frühzeitige Detektion von Oberflächenschäden auf der Kugelgewindetriebspindel mit einem elektromechanischen Kamerasystem in Kombination mit Deep-Learning-basierten Modellen, um entsprechende Wartungsmaßnahmen abzuleiten.   Condition monitoring of plants, machines and their components is a central topic of Industry 4.0. Unforeseeable failures of machine tools are often caused by wear, resulting in failure of ball screws and subsequent surface disruptions. This article describes how image-based monitoring of ball screws by an electronic camera system in combination with deep learning-based models enable the early detection of surface disruptions and to derive appropriate and preventive maintenance measures.

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Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancer

Mathematical and Computational Oncology - Lecture Notes in Computer Science ◽

10.1007/978-3-030-64511-3_3 ◽

2020 ◽

pp. 25-31

Author(s):

Roshan Alex Welikala ◽

Paolo Remagnino ◽

Jian Han Lim ◽

Chee Seng Chan ◽

Senthilmani Rajendran ◽

...

Keyword(s):

Deep Learning ◽

Oral Cancer ◽

Early Detection ◽

Fine Tuning ◽

Learning Architectures

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Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

PLoS ONE ◽

10.1371/journal.pone.0256500 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256500

Author(s):

Maleika Heenaye-Mamode Khan ◽

Nazmeen Boodoo-Jahangeer ◽

Wasiimah Dullull ◽

Shaista Nathire ◽

Xiaohong Gao ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Deep Learning ◽

Early Detection ◽

Convolutional Neural Network ◽

Learning Model ◽

Learning Rate ◽

Deep Convolutional Neural Network ◽

Deep Learning Model

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

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