Automatic Seeded Region Growing with Level Set Technique Used for Segmentation of Pancreas

Paithane, Pradip M.; Kakarwal, S. N.; Kurmude, D. V.

doi:10.1007/978-3-030-73689-7_36

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Hence, for the proposed system's behavior, 7 semantic variables are identified. Out of these 6, six is input variables namely-turbidity (T), dissolved oxygen (DO), biochemical oxygen demand (BOC), pH value (pH), Nitrate(NO-3) and Total Dissolved Solid (TDS) and one is output variable drinking water quality [20]. Each point in input space is justified in its evolution through the application of triangle and trapezoidal association functions, which are connected to association cost among 0 to 1 [21].…”

Section: Fuzzy Water Quality Index (Fwqi)mentioning

confidence: 99%

Computation of Groundwater Quality of Baramati with the help of Fuzzy Water Quality Index (FWQI)

Wagh,

Paithane,

Sangale

et al. 2023

Int. Res. J. multidiscip. Technovation

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One of the natural resources having the latent for home, agrarian, and engineering use is surface and groundwater. Due to humanoid and certain natural reasons, the eminence of the groundwater serving Baramati City and Tehsil has deteriorated. Agriculture is using more pesticides and fertilizer, therefore this supply is being affected. Municipal water pollution can be caused by “septic boilers”, “bathe sewage”, “polluted aquatic”, “improper discarded management”, “public excretion”, “improper waste structure”, “public sewage discharges”, and “unorganized solid waste disposal”. The current study will improve the accuracy of the water quality index for areas in Baramati Tehsil that are affected by industry and drinking water supplies. The groundwater zones were created using a weighted index overlay analysis, which assigned weights based on several classes of individual water quality metrics and drinking water standards. Based on few observations, fuzzy logic offers an effective and practical tool for categorizing drinking water quality. This study's objective is to provide a fuzzy logic-based water quality indicator for basin-wide reservoirs. For a weight-based fuzzy quality index, a minimum of 6 physico-chemicals are needed.

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Section: Fuzzy Water Quality Index (Fwqi)mentioning

confidence: 99%

Computation of Groundwater Quality of Baramati with the help of Fuzzy Water Quality Index (FWQI)

Wagh,

Paithane,

Sangale

et al. 2023

Int. Res. J. multidiscip. Technovation

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show abstract

“…Putting all data linked to agricultural happenings on a blockchain allows for the creation of a dependable and transparent system. Farmers also have rapid access to information on a variety of areas, such as seed quality, weather and environment, payments, soil moisture, demand, and sale price [11].…”

Section: I1 Key Contributionsmentioning

confidence: 99%

Random forest algorithm use for crop recommendation

Paithane

2023

ITEGAM

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The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%.

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“…DICOM medical images are converted to PNG image format. The view of scans images was axial, sagittal and coronoal with 1,5 mm or 3 mm thickness available [9]. Human expert labeling was performed under guideline of certified radiologist.…”

Section: Image Dataset and Ground Truth Labelingmentioning

confidence: 99%

“…Basic phenomena are to adjustment of portable pathway of a two-dimensional arc into portable path of a 3D surface. In this, level Set is actively participate toward indication of arcs as zero levelset of high dimension hyper-superficial [9]. Challenge of such exercise suggestions supplementary faultless algebraic execution also operates topological variation without any prob-lems [8].…”

Section: Levelsets Algorithmmentioning

confidence: 99%

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Automatic pancreas segmentation using ResNet-18 deep learning approach

Kakarwal¹,

Paithane²

2022

SRIT

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The accurate pancreas segmentation process is essential in the early detection of pancreatic cancer. The pancreas is situated in the abdominal cavity of the human body. The abdominal cavity contains the pancreas, liver, spleen, kidney, and adrenal glands. Sharp and smooth detection of the pancreas from this abdominal cavity is a challenging and tedious job in medical image investigation. Top-down approaches like Novel Modified K-means Fuzzy clustering algorithm (NMKFCM), Scale Invariant Feature Transform (SIFT), Kernel Density Estimator (KDE) algorithms were applied for pancreas segmentation in the early days. Recently, Bottom-up method has become popular for pancreas segmentation in medical image analysis and cancer diagnosis. LevelSet algorithm is used to detect the pancreas from the abdominal cavity. The deep learning, bottom-up approach performance is better than another. Deep Residual Network (ResNet-18) deep learning, bottom-up approach is used to detect accurate and sharp pancreas from CT scan medical images. 18 layers are used in the architecture of ResNet-18. The automatic pancreas and kidney segmentation is accurately extracted from CT scan images. The proposed method is applied to the medical CT scan images dataset of 82 patients. 699 images and 150 images with different angles are used for training and testing purposes, respectively. ResNet-18 attains a dice similarity index value up to 98.29±0.63, Jaccard Index value up to 96.63±01.25, Bfscore value up to 84.65±03.96. The validation accuracy of the proposed method is 97.01%, and the loss rate value achieves up to 0.0010. The class imbalance problem is solved by class weight and data augmentation.

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Automatic Seeded Region Growing with Level Set Technique Used for Segmentation of Pancreas

Cited by 4 publications

References 4 publications

Computation of Groundwater Quality of Baramati with the help of Fuzzy Water Quality Index (FWQI)

Computation of Groundwater Quality of Baramati with the help of Fuzzy Water Quality Index (FWQI)

Random forest algorithm use for crop recommendation

Automatic pancreas segmentation using ResNet-18 deep learning approach

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