A new approach for breast abnormality detection based on thermography

Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method  showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Food Protein-based Core-shell Nanocarriers for Oral Drug Delivery Applications: (Influence of Shell Composition on \u3cem\u3eIn vitro\u3c/em\u3e and \u3cem\u3eIn vivo\u3c/em\u3e Functional Performance of Zein Nanocarriers

Oral delivery is the most preferred route for drug administration. Oral drug delivery is limited by poor physicochemical properties of drugs and physiological barriers in the gastrointestinal tract. To this end, there is a need for developing new carrier systems to enhance the oral bioavailability of poorly absorbed molecules. Food-grade biopolymers are attractive materials for developing drug delivery carriers’ due to their unique properties and proven safety. Six different core-shell nanocarriers were prepared using food-grade biopolymers including zein-casein (ZC) nanoparticles, zein-lactoferrin (ZLF) nanoparticles, zein-β-lactoglobulin (ZLG) nanoparticles, zein-whey protein isolate (ZWP) nanoparticles, zein-pluronic-lecithin (ZPL) nanoparticles and zein-PEG (ZPEG) micelles. The study was aimed at systematically investigating the influence of shell composition on the functional performance of core-shell nanocarriers for oral drug delivery applications. The first goal was to develop and study the structure-function relationship of coreshell nanocarriers for oral drug delivery applications. Nile red (NR) and Cy 5.5 were used as model dyes for this study. The particle size of the nanocarriers ranged from 100 to 250 nm, and the nanocarrier had a uniform size distribution as evidenced from the low PDI (0.08 to 0.3). The zeta potential values varied from -10 to 30 mV depending on the shell composition. The core-shell structure of the nanocarrier was confirmed by Transmission Electron Microscopy (TEM). The nanocarriers sustained the release of NR in simulated gastric and intestinal fluids. NR release from the nanocarriers predominantly followed Peppas model which indicates the diffusion of NR from nanocarriers by polymer erosion by hydrolytic or enzymatic cleavage. NR release from ZPEG micelles followed first order release kinetics. The nanocarriers were taken up by endocytosis in Caco-2 cells, which is an established model for intestinal permeability studies. ZLG nanocarriers showed the highest permeability across Caco-2 cell monolayers, while ZC nanoparticles showed the lowest permeability among the six formulations. ZPEG micelles also showed P-gp inhibitory activity. All the nanocarriers were found to have bioadhesive properties. Among the six different nanocarriers, ZLG and ZWP nanocarriers showed significantly higher bioadhesive property. In-vivo biodistribution of the nanocarriers was studied using Cy 5.5, a near-IR dye and all the formulations showed longer retention in the rat gastrointestinal tract compared to the free dye. Among the formulations, ZLG and ZWP nanoparticles were retained longest in the rat gastrointestinal tract (≥24 hours). All the nanocarriers were found to be non-immunogenic on oral administration to mice. The second goal was to investigate the use of core-shell nanocarriers for oral delivery of a model antiretroviral drug, lopinavir (LPV). LPV is a first-line protease inhibitor used for the treatment of HIV infections, especially in children. The drug has poor oral bioavailability due to its poor water solubility, poor membrane permeability and firstpass metabolism in the intestine. LPV is a substrate for the CYP3A4 enzyme and hence is used in combination with ritonavir (a CYP3A4 inhibitor) to boost the oral bioavailability of LPV. The current pediatric oral liquid formulation contains LPV and ritonavir (RTV) in a mixture of high proportion of propylene glycol and alcohol. The main goal was to test the feasibility of developing a water dispersible RTV free pediatric formulation of LPV using zein-based core-shell nanocarriers. The impact of shell composition on the functional properties of LPV loaded nanocarriers was evaluated in vitro and in vivo. The encapsulation efficiency for LPV was above 70% in all the nanocarriers, and ZPL nanoparticles showed the highest encapsulation efficiency (87.92±7.19%). The loading efficiency ranged from 2 to 5% based on the shell composition. The release of LPV was sustained both in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF) for 24 hours. To test the feasibility of developing a food sprinkle formulation, the compatibility of the nanoformulations with model food matrices were studied. The nanocarriers were stable when incubated in food matrices (milk and applesauce) andZC\u3eZLF\u3eZWP\u3eZLG\u3eZPL. In vivo pharmacokinetic study in rats showed that the oral bioavailability of LPV increased by 2-fold compared to marketed LPV/RTV liquid formulation (Kaletra®). The highest oral bioavailability was obtained with LPV loaded ZPEG micelles followed by ZWP and ZLG nanoparticles. Highest plasma concentration (Cmax) of LPV was achieved with ZPEG micelles which was comparable to Kaletra® formulation. The extent of absorption (AUC) of LPV was in the following decreasing order of ZPEG\u3eZWP\u3eZLG\u3eKaletra®\u3efree LPV. Multiple dose PK study further demonstrated that similar or higher steady-state plasma concentration can be obtained using ZPEG micelles compared to Kaletra®. Findings from this chapter concludes that zein-based nanocarriers can be used to develop ritonavir free LPV formulation which will ultimately reduce the total drug load and drug-drug interaction in the treatment of HIV infection. The last objective was to demonstrate the feasibility of using zein-based core-shell nanocarriers for oral delivery of fenretinide, an investigational anti-cancer molecule. Fenretinide has been found to be effective against several cancers including pediatric neuroblastoma, However, the clinical development of fenretinide is limited by its poor physicochemical properties. Fenretinide is a poorly soluble and poor permeable anti-cancer agent. Further, the compound has poor chemical stability. The encapsulation efficiency for fenretinide was above 70% in all the nanocarriers and zein-β-casein (ZC) nanoparticles showed the highest encapsulation efficiency (90±0.091%). The release of fenretinide was sustained both in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF) for 24 hours. The nanocarriers were stable when incubated in food matrices (milk and applesauce), and less than 30% of fenretinide was released after incubation for 1 hour in food matrices. About 60% of fenretinide was released over 24 hours when the nanocarriers was transferred from food matrices to SGF and SIF. The nanocarriers enhanced the permeability of fenretinide across the Caco-2 cell monolayers from 1x10-6 to 72.42x10-6 cm/s. The order of permeability of fenretinide loaded nanocarriers was found to be in the following decreasing order ZPL\u3eZLG\u3eZC\u3eZWP\u3eZLF\u3eZPEG. Among others tested for single dose PK study of fenretinide, ZLG nanocarriers showed the highest oral bioavailability of fenretinide (6-fold) compared to free fenretinide suspension. Nanocarriers increased the elimination half-life (t1/2) by 2- to 4-fold. ZPL nanocarriers showed the highest Cmax (0.61 μg/mL) of fenretinide, while fenretinide loaded ZC nanocarriers showed the lowest Cmax (0.23 μg/mL). Nanocarriers showed the following decreasing rank order for relative oral bioavailability, ZWP\u3eZLG\u3eZPL\u3eZC, indicating that shell composition has a significant influence on the oral bioavailability. Further, multiple dose pharmacokinetic (PK) studies of fenretinide and fenretinide loaded zeinpluronic- lecithin (ZPL) nanocarriers was performed. The pharmacokinetics of twice a day free fenretinide suspension was compared with once a fenretinide loaded ZPL nanocarriers. The steady state concentration of fenretinide and fenretinide loaded ZPL nanocarriers was achieved at around 50-hours. However, the steady-state plasma concentration of fenretinide from the ZPL nanocarriers was 5-fold higher compared to free fenretinide suspension. Overall, the outcomes from this study demonstrate the structure-function relationship of core-shell protein nanocarriers for oral drug delivery applications. The findings from this study can be used to develop food protein based oral drug delivery systems with specific functional attributes for various oral drug delivery applications

Formulation development, manufacture and evaluation of hydralazine hydrochloride microspheres

Expected release date-April 201

Preclinical Evaluation of Lipid-Based Nanosystems

The use of lipid-based nanosystems, including lipid nanoparticles (solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC)), nanoemulsions, and liposomes, among others, is widespread. Several researchers have described the advantages of different applications of these nanosystems. For instance, they can increase the targeting and bioavailability of drugs, improving therapeutic effects. Their use in the cosmetic field is also promising, owing to their moisturizing properties and ability to protect labile cosmetic actives. Thus, it is surprising that only a few lipid-based nanosystems have reached the market. This can be explained by the strict regulatory requirements of medicines and the occurrence of unexpected in vivo failure, which highlights the need to conduct more preclinical studies.Current research is focused on testing the in vitro, ex vivo, and in vivo efficacy of lipid-based nanosystems to predict their clinical performance. However, there is a lack of method validation, which compromises the comparison between different studies.This book brings together the latest research and reviews that report on in vitro, ex vivo, and in vivo preclinical studies using lipid-based nanosystems. Readers can find up-to-date information on the most common experiments performed to predict the clinical behavior of lipid-based nanosystems. A series of 15 research articles and a review are presented, with authors from 15 different countries, which demonstrates the universality of the investigations that have been carried out in this area