First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

Abstract

Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of 0.876 ± 0.026 in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

“…The research used a total of 102 topological-based, morphological-based, and texture-based selected features from each tissue patch so that quantifying the arrangement of glandular and nuclei structures within histopathological images of prostate cancer tissues. Another recent research in [ 27 ], provided an automatic system able to accurately detect specific areas susceptible to be cancerous through presenting a novel method, a combination of topological-based, morphological-based, and texture-based feature selection for addressing the hand-crafted feature selection stage.…”

“…Thus, the whole histopathology image is often divided into partial regions of about 1024 × 1024 pixels called patches, where each patch is examined apart, such as detecting region-of-interests [ 56 ]. Thus, many studies such as [ 16 , 24 , 25 , 26 , 27 , 48 , 57 , 58 ] presented in this survey, especially those dealing with deep learning applied patching technique to overcome the extremely large histopathological images.…”

“…In the last decade, researchers have turned their focus towards the development of new deep learning techniques as they outperform conventional machine learning techniques in diverse fields and not only HI image analysis. To date, many of these ML techniques have been supplanted by DL, and an abundance of work has evaluated the use of deep learning techniques on HI of prostate cancer [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Moreover, studies that employ an ensemble of DL techniques and ML techniques gave better results [ 34 ].…”

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

“…With regard to the objectives to be addressed, some works focus just on the detection of prostate cancer against non-cancerous tissue [13,17] or on the first-stage prostate cancer detection [22]. A full analysis of Gleason grades from 3 to 5 is usually limited by the size of the collected database, and the low prevalence of Gleason grade 5.…”

Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features

“…MLP is a supervised classification system that consists of at least three layers of nodes: the first layer is the input layer, the middle layer is the hidden layer, and the last layer is the output layer. Input and output layers are used to feed in data and obtain the output results, respectively [23][24][25][26]. However, the hidden layer can be modified to increase the complexity of the model.…”