Detection of Elephantiasis patients using Image processing and Classification methods

"The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cases are caused by the parasite wuchereria bancrofti and the remaining is due to brugia malayi parasite worm. A symptom of the infection includes tissue swelling, retention of fluid, genital diseases, and acute disease. This research investigates on detection of elephantiasis patients using image processing and classification. This research work represents a patient with elephantiasis disease and without elephantiasis disease. For this research 35, sample images were examined using image processing with classification methods. The present research represents 91.4% accuracy of detection with patients having elephantiasis disease.

Detection of Elephantiasis patients using Image processing and Classification methods

"The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cases are caused by the parasite wuchereria bancrofti and the remaining is due to brugia malayi parasite worm. A symptom of the infection includes tissue swelling, retention of fluid, genital diseases, and acute disease. This research investigates on detection of elephantiasis patients using image processing and classification. This research work represents a patient with elephantiasis disease and without elephantiasis disease. For this research 35, sample images were examined using image processing with classification methods. The present research represents 91.4% accuracy of detection with patients having elephantiasis disease.

Detection of Elephantiasis patients using Image processing and Classification methods

"The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cases are caused by the parasite wuchereria bancrofti and the remaining is due to brugia malayi parasite worm. A symptom of the infection includes tissue swelling, retention of fluid, genital diseases, and acute disease. This research investigates on detection of elephantiasis patients using image processing and classification. This research work represents a patient with elephantiasis disease and without elephantiasis disease. For this research 35, sample images were examined using image processing with classification methods. The present research represents 91.4% accuracy of detection with patients having elephantiasis disease.

Detection of Elephantiasis patients using Image processing and Classification methods

"The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cases are caused by the parasite wuchereria bancrofti and the remaining is due to brugia malayi parasite worm. A symptom of the infection includes tissue swelling, retention of fluid, genital diseases, and acute disease. This research investigates on detection of elephantiasis patients using image processing and classification. This research work represents a patient with elephantiasis disease and without elephantiasis disease. For this research 35, sample images were examined using image processing with classification methods. The present research represents 91.4% accuracy of detection with patients having elephantiasis disease.

HASH(0x563d4401e8e0)

HASH(0x563d43fa4380)HASH(0x563d440ddb00

Lymphatic Filariasis detection in microscopic images

In Africa, the propagation of parasites like the lymphatic filariasis is complicatingseriously the efforts of health professionals to cure certain diseases. Although there aremedicines capable to treat the lymphatic filariasis, this needs to be discovered firstly which isnot always an easy task having into account that in most countries affected by this disease it canonly be detected at night (nocturne). The lymphatic filariasis is then, a parasitical infectionwhich can originate changes or ruptures in the lymphatic system as well as an abnormal growthof certain areas of the body causing pain, incapacity and social stigma.Approximately 1.23 billion people in 58 countries from all over the world are threatenedby this disease which requires a preventive treatment to stop its propagation which makes iteven more important for the existence of a mechanism that is less costly and more agile in theanalysis of a blood smear to verify the existence of microfilariae (little worms that are producedby other adult worms while housed in the lymphatic system).The lymphatic filariasis is caused by an infection with nematodes ("roundworms") of theFilariodidea family in which three types are inserted: Wuchereria Bancroft, responsible for 90%of all cases; Brugia Malayi, responsible for almost every remaining; B.Timori also causing thedisease. All three have characteristics that can differentiate them which allow them to beidentified.The current identification process of the disease consists on the analysis of microfilariae ina blood smear with a blood sample through a microscope and its identification by the observer.Taking this into account, it is intended to develop image analysis and processingtechniques for the recognition and counting of the two principal types of filarial worms from athin blood smear, a smartphone and a portable microscope making the detection possiblewithout the need of a health professional and consequent automation of the process. To makethis possible an adapter smartphone-microscope can be used to obtain an image with themagnification of 40x3. The images can then be analyzed in a server or in the smartphone, if ithas enough processing for it. It is expected from this process that the need to resort to labs toprocess the blood smear gets fulfilled making the process more accessible and agile instead ofcostly and slow.For the detection of the parasites from the acquired images it is intended to implement,experiment and choose the more adequate operations. These comprise pre-processing operationswith the goal to enhance the acquired images and eliminate possible artifacts prevenient fromthe acquisition system. However, the principal operations should be those that allow theverification of existence or nonexistence, recognition and classification of the pretendedparasites. Processing and analysis techniques that are common in these processes are based inthe extraction of features (e.g. SIRF, SURF, and FLANN) template similarity, edge detectionand description of contours and recognition of statistical patterns.Once detected and recognized one or more parasites and its types should be defined andused a rule to declare the presence of the disease and its stage

The burden of disease due to onchocerciasis and lymphatic filariasis in Africa:past, present and futureNNaattaalliiee

The dissertation titled “The burden of disease due to onchocerciasis and lymphatic filariasis in Africa: past, present and future” concerns two important parasitic infectious diseases of the tropics. Both infections are caused by a specific species of filarial worms and can cause a broad spectrum of clinical morbidity in the affected population, including blindness (onchocerciasis) and elephantiasis/elephant legs (Lymphatic Filariasis, LF). The research focused on how the disease burden of onchocerciasis and LF, in terms of total cases and life years with disabilities (DALYs), has changed since the introduction of large-scale mass treatment programs in Africa and what burden will remain in 2030. DALYs are a measure that takes into account both loss of quality of life and premature death. To calculate this, the association between infection and morbidity at the community level prior to mass treatment was first quantified. Subsequently, mathematical models have been used to calculate the impact of interventions on initial levels of infection by onchocerciasis and LF across Africa. The study predicted past, current and future disease prevalence, as well as the number of cases and lost DALYs due to onchocerciasis and LF. This work concludes that interventions have a remarkable impact on the prevention of onchocerciasis and LF, although by 2030 millions of people will still suffer from morbidity in Africa from one or both infections